A systematic review of prediction models used in tuberculosis contact tracing

Abstract Background Contact tracing forms a key part of tuberculosis (TB) control in high-income, low-incidence settings. It aims to reduce morbidity, mortality and onward transmission of TB. Contact tracing is a complex and resource intensive intervention. Risk assessment of contacts is needed to ensure appropriate allocation of resources and greatest possible impact. Current prioritisation procedures are based on expert opinion and consensus. Prognostic prediction models offer a way to synthesise evidence about this decision. Methods We searched Medline, Embase, BNI, CINAHL, HMIC, and the Cochrane Library for peer reviewed publications in English about TB contact tracing prediction models. Studies were included if there was statistical combination of predictors. No date, age or other restrictions were applied. Study selection was carried out by two independent reviewers. Data were extracted using the CHARMS checklist and studies evaluated for risk of bias using PROBAST. Results Five reports were selected from a total of 16,585 non-identical returns. Each study was carried out in demographically distinct settings (Peru, USA, France, Taiwan). The choice and definition of outcomes and predictors varied. All the models included external validation and some included internal validation. Calibration and discrimination measures were variably reported. The models were at high risk of bias due to challenges in defining TB disease and due to statistical approaches taken: there was poor reporting of sample size considerations, universal use of univariable analysis to select predictors, and dichotomisation of data. There were some concerns about applicability due to differing populations and diagnostic approaches. None of the models included social risk factors. Conclusions The use of existing models is problematic. There are constraints upon resources which means that contact tracing needs to be carried out efficiently. A robust prediction model is urgently needed to achieve this. Key messages Contact tracing for tuberculosis would benefit from more robust prioritisation tools to save resources and increase impact. Existing prognostic prediction models are at high risk of bias and there are concerns about applicability in high-income, low-incidence settings.

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Predicting relapse or recurrence of depression: systematic review of prognostic models

The British Journal of Psychiatry ◽

10.1192/bjp.2021.218 ◽

2022 ◽

pp. 1-11

Author(s):

Andrew S. Moriarty ◽

Nicholas Meader ◽

Kym I. E. Snell ◽

Richard D. Riley ◽

Lewis W. Paton ◽

...

Keyword(s):

High Risk ◽

Validation Studies ◽

Prediction Models ◽

Assessment Tool ◽

External Validation ◽

Model Development ◽

Risk Of Bias ◽

Prognostic Models ◽

Cochrane Library ◽

Sustained Remission

Background Relapse and recurrence of depression are common, contributing to the overall burden of depression globally. Accurate prediction of relapse or recurrence while patients are well would allow the identification of high-risk individuals and may effectively guide the allocation of interventions to prevent relapse and recurrence. Aims To review prognostic models developed to predict the risk of relapse, recurrence, sustained remission, or recovery in adults with remitted major depressive disorder. Method We searched the Cochrane Library (current issue); Ovid MEDLINE (1946 onwards); Ovid Embase (1980 onwards); Ovid PsycINFO (1806 onwards); and Web of Science (1900 onwards) up to May 2021. We included development and external validation studies of multivariable prognostic models. We assessed risk of bias of included studies using the Prediction model risk of bias assessment tool (PROBAST). Results We identified 12 eligible prognostic model studies (11 unique prognostic models): 8 model development-only studies, 3 model development and external validation studies and 1 external validation-only study. Multiple estimates of performance measures were not available and meta-analysis was therefore not necessary. Eleven out of the 12 included studies were assessed as being at high overall risk of bias and none examined clinical utility. Conclusions Due to high risk of bias of the included studies, poor predictive performance and limited external validation of the models identified, presently available clinical prediction models for relapse and recurrence of depression are not yet sufficiently developed for deploying in clinical settings. There is a need for improved prognosis research in this clinical area and future studies should conform to best practice methodological and reporting guidelines.

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The Unrealised Potential for Predicting Pregnancy Complications in Women with Gestational Diabetes: A Systematic Review and Critical Appraisal

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17093048 ◽

2020 ◽

Vol 17 (9) ◽

pp. 3048 ◽

Cited By ~ 1

Author(s):

Shamil D. Cooray ◽

Lihini A. Wijeyaratne ◽

Georgia Soldatos ◽

John Allotey ◽

Jacqueline A. Boyle ◽

...

Keyword(s):

Systematic Review ◽

Body Mass Index ◽

High Risk ◽

Gestational Diabetes ◽

Body Mass ◽

Pregnancy Complications ◽

Critical Appraisal ◽

Prediction Models ◽

Risk Of Bias ◽

Prognostic Prediction

Gestational diabetes (GDM) increases the risk of pregnancy complications. However, these risks are not the same for all affected women and may be mediated by inter-related factors including ethnicity, body mass index and gestational weight gain. This study was conducted to identify, compare, and critically appraise prognostic prediction models for pregnancy complications in women with gestational diabetes (GDM). A systematic review of prognostic prediction models for pregnancy complications in women with GDM was conducted. Critical appraisal was conducted using the prediction model risk of bias assessment tool (PROBAST). Five prediction modelling studies were identified, from which ten prognostic models primarily intended to predict pregnancy complications related to GDM were developed. While the composition of the pregnancy complications predicted varied, the delivery of a large-for-gestational age neonate was the subject of prediction in four studies, either alone or as a component of a composite outcome. Glycaemic measures and body mass index were selected as predictors in four studies. Model evaluation was limited to internal validation in four studies and not reported in the fifth. Performance was inadequately reported with no useful measures of calibration nor formal evaluation of clinical usefulness. Critical appraisal using PROBAST revealed that all studies were subject to a high risk of bias overall driven by methodologic limitations in statistical analysis. This review demonstrates the potential for prediction models to provide an individualised absolute risk of pregnancy complications for women affected by GDM. However, at present, a lack of external validation and high risk of bias limit clinical application. Future model development and validation should utilise the latest methodological advances in prediction modelling to achieve the evolution required to create a useful clinical tool. Such a tool may enhance clinical decision-making and support a risk-stratified approach to the management of GDM. Systematic review registration: PROSPERO CRD42019115223.

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Applications of Artificial Intelligence and Machine Learning in Diagnosis and Prognosis of COVID-19 infection: A systematic review

Frontiers in Health Informatics ◽

10.30699/fhi.v10i1.321 ◽

2021 ◽

Vol 10 (1) ◽

pp. 93

Author(s):

Mahdieh Montazeri ◽

Ali Afraz ◽

Mitra Montazeri ◽

Sadegh Nejatzadeh ◽

Fatemeh Rahimi ◽

...

Keyword(s):

Machine Learning ◽

High Risk ◽

Prediction Models ◽

External Validation ◽

Area Under The Curve ◽

Risk Of Bias ◽

Prognostic Models ◽

Discriminative Performance ◽

Internal Validation ◽

Diagnostic Models

Introduction: Our aim in this study was to summarize information on the use of intelligent models for predicting and diagnosing the Coronavirus disease 2019 (COVID-19) to help early and timely diagnosis of the disease.Material and Methods: A systematic literature search included articles published until 20 April 2020 in PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv databases. The search strategy consisted of two groups of keywords: A) Novel coronavirus, B) Machine learning. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. Studies were critically reviewed for risk of bias using prediction model risk of bias assessment tool.Results: We gathered 1650 articles through database searches. After the full-text assessment 31 articles were included. Neural networks and deep neural network variants were the most popular machine learning type. Of the five models that authors claimed were externally validated, we considered external validation only for four of them. Area under the curve (AUC) in internal validation of prognostic models varied from .94 to .97. AUC in diagnostic models varied from 0.84 to 0.99, and AUC in external validation of diagnostic models varied from 0.73 to 0.94. Our analysis finds all but two studies have a high risk of bias due to various reasons like a low number of participants and lack of external validation.Conclusion: Diagnostic and prognostic models for COVID-19 show good to excellent discriminative performance. However, these models are at high risk of bias because of various reasons like a low number of participants and lack of external validation. Future studies should address these concerns. Sharing data and experiences for the development, validation, and updating of COVID-19 related prediction models is needed.

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The Use of Prognostic Prediction Models for Mortality or Clinical Deterioration among Hospitalized and Non-hospitalized Adults with COVID-19: A Systematic Review

Acta Medica Philippina ◽

10.47895/amp.vi0.4195 ◽

2021 ◽

Author(s):

Patricia Pauline M. Remalante-Rayco ◽

Evelyn Osio-Salido

Keyword(s):

Systematic Review ◽

Emergency Department ◽

Early Warning ◽

Prediction Models ◽

External Validation ◽

Risk Of Bias ◽

Clinical Deterioration ◽

Early Warning Score ◽

Good Prediction ◽

Prediction Of Mortality

Objective. To assess the performance of prognostic models in predicting mortality or clinical deterioration among patients with COVID-19, both hospitalized and non-hospitalized Methods. We conducted a systematic review of the literature until March 8, 2021. We included models for the prediction of mortality or clinical deterioration in COVID-19 with external validation. We used the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the GRADEpro Guideline Development Tool (GDT) to assess the evidence obtained. Results. We reviewed 33 cohort studies. Two studies had a low risk of bias, four unclear risks, and 27 with a high risk of bias due to participant selection and analysis. For the outcome of mortality, the QCOVID model had excellent prediction with high certainty of evidence but was specific for use in England. The COVID Outcome Prediction in the Emergency Department (COPE) model, the 4C Mortality Score, the Age, BUN, number of comorbidities, CRP, SpO2/FiO2 ratio, platelet count, heart rate (ABC2-SPH) risk score, the Confusion Urea Respiration Blood Pressure (CURB-65) severity score, the Rapid Emergency Medicine Score (REMS), and the Risk Stratification in the Emergency Department in Acutely Ill Older Patients (RISE UP) score had fair to good prediction of death among inpatients, while the quick Sepsis-related Organ Failure Assessment (qSOFA) score had poor to fair prediction. The certainty of evidence for these models was very low to low. For the outcome of clinical deterioration, the 4C Deterioration Score had fair prediction, the National Early Warning Score 2 (NEWS2) score poor to good, and the Modified Early Warning Score (MEWS) had poor prediction. The certainty of evidence for these three models was also very low to low. None of these models had been validated in the Philippine setting. Conclusion. The QCOVID, COPE, ABC2-SPH, 4C, CURB-65, REMS, RISE-UP models for prediction of mortality and the 4C Deterioration and NEWS2 models for prediction of clinical deterioration are potentially useful but need to be validated among patients with COVID-19 of varying severity in the Philippine setting.

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P–419 On prognosis after unexplained recurrent pregnancy losses (RPL); a systematic review and external validation of clinical prediction models

Human Reproduction ◽

10.1093/humrep/deab130.418 ◽

2021 ◽

Vol 36 (Supplement_1) ◽

Author(s):

A Youssef

Keyword(s):

Systematic Review ◽

Supportive Care ◽

Prediction Model ◽

Pregnancy Outcome ◽

Prediction Models ◽

External Validation ◽

Model Development ◽

Predictive Performance ◽

Pregnancy Rates ◽

Cochrane Library

Abstract Study question Which models that predict pregnancy outcome in couples with unexplained RPL exist and what is the performance of the most used model? Summary answer We identified seven prediction models; none followed the recommended prediction model development steps. Moreover, the most used model showed poor predictive performance. What is known already RPL remains unexplained in 50–75% of couples For these couples, there is no effective treatment option and clinical management rests on supportive care. Essential part of supportive care consists of counselling on the prognosis of subsequent pregnancies. Indeed, multiple prediction models exist, however the quality and validity of these models varies. In addition, the prediction model developed by Brigham et al is the most widely used model, but has never been externally validated. Study design, size, duration We performed a systematic review to identify prediction models for pregnancy outcome after unexplained RPL. In addition we performed an external validation of the Brigham model in a retrospective cohort, consisting of 668 couples with unexplained RPL that visited our RPL clinic between 2004 and 2019. Participants/materials, setting, methods A systematic search was performed in December 2020 in Pubmed, Embase, Web of Science and Cochrane library to identify relevant studies. Eligible studies were selected and assessed according to the TRIPOD) guidelines, covering topics on model performance and validation statement. The performance of predicting live birth in the Brigham model was evaluated through calibration and discrimination, in which the observed pregnancy rates were compared to the predicted pregnancy rates. Main results and the role of chance Seven models were compared and assessed according to the TRIPOD statement. This resulted in two studies of low, three of moderate and two of above average reporting quality. These studies did not follow the recommended steps for model development and did not calculate a sample size. Furthermore, the predictive performance of neither of these models was internally- or externally validated. We performed an external validation of Brigham model. Calibration showed overestimation of the model and too extreme predictions, with a negative calibration intercept of –0.52 (CI 95% –0.68 – –0.36), with a calibration slope of 0.39 (CI 95% 0.07 – 0.71). The discriminative ability of the model was very low with a concordance statistic of 0.55 (CI 95% 0.50 – 0.59). Limitations, reasons for caution None of the studies are specifically named prediction models, therefore models may have been missed in the selection process. The external validation cohort used a retrospective design, in which only the first pregnancy after intake was registered. Follow-up time was not limited, which is important in counselling unexplained RPL couples. Wider implications of the findings: Currently, there are no suitable models that predict on pregnancy outcome after RPL. Moreover, we are in need of a model with several variables such that prognosis is individualized, and factors from both the female as the male to enable a couple specific prognosis. Trial registration number Not applicable

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Prognostic prediction models for pregnancy complications in women with gestational diabetes: a protocol for systematic review, critical appraisal and meta-analysis

Systematic Reviews ◽

10.1186/s13643-019-1151-0 ◽

2019 ◽

Vol 8 (1) ◽

Cited By ~ 7

Author(s):

Shamil D. Cooray ◽

Jacqueline A. Boyle ◽

Georgia Soldatos ◽

Lihini A. Wijeyaratne ◽

Helena J. Teede

Keyword(s):

Systematic Review ◽

Decision Making ◽

Gestational Diabetes ◽

Pregnancy Complications ◽

Prediction Models ◽

Absolute Risk ◽

Meta Analysis ◽

External Validation ◽

The Absolute ◽

Prognostic Prediction

Abstract Background Gestational diabetes (GDM) is increasingly common and has significant implications during pregnancy and for the long-term health of the mother and offspring. However, it is a heterogeneous condition with inter-related factors including ethnicity, body mass index and gestational weight gain significantly modifying the absolute risk of complications at an individual level. Predicting the risk of pregnancy complications for an individual woman with GDM presents a useful adjunct to therapeutic decision-making and patient education. Diagnostic prediction models for GDM are prevalent. In contrast, prediction models for risk of complications in those with GDM are relatively novel. This study will systematically review published prognostic prediction models for pregnancy complications in women with GDM, describe their characteristics, compare performance and assess methodological quality and applicability. Methods Studies will be identified by searching MEDLINE and Embase electronic databases. Title and abstract screening, full-text review and data extraction will be completed independently by two reviewers. The included studies will be systematically assessed for risk of bias and applicability using appropriate tools designed for prediction modelling studies. Extracted data will be tabulated to facilitate qualitative comparison of published prediction models. Quantitative data on predictive performance of these models will be synthesised with meta-analyses if appropriate. Discussion This review will identify and summarise all published prognostic prediction models for pregnancy complications in women with GDM. We will compare model performance across different settings and populations with meta-analysis if appropriate. This work will guide subsequent phases in the prognosis research framework: further model development, external validation and model updating, and impact assessment. The ultimate model will estimate the absolute risk of pregnancy complications for women with GDM and will be implemented into routine care as an evidence-based GDM complication risk prediction model. It is anticipated to offer value to women and their clinicians with individualised risk assessment and may assist decision-making. Ultimately, this systematic review is an important step towards a personalised risk-stratified model-of-care for GDM to allow preventative and therapeutic interventions for the maximal benefit to women and their offspring, whilst sparing expense and harm for those at low risk. Systematic review registration PROSPERO registration number CRD42019115223

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Prognostic models for adverse pregnancy outcomes in low-income and middle-income countries: a systematic review

BMJ Global Health ◽

10.1136/bmjgh-2019-001759 ◽

2019 ◽

Vol 4 (5) ◽

pp. e001759 ◽

Cited By ~ 1

Author(s):

Tessa Heestermans ◽

Beth Payne ◽

Gbenga Ayodele Kayode ◽

Mary Amoakoh-Coleman ◽

Ewoud Schuit ◽

...

Keyword(s):

Low Income ◽

Perinatal Death ◽

External Validation ◽

Perinatal Outcomes ◽

Risk Of Bias ◽

Prognostic Models ◽

Cochrane Library ◽

Middle Income ◽

Middle Income Countries ◽

Adverse Pregnancy

IntroductionNinety-nine per cent of all maternal and neonatal deaths occur in low-income and middle-income countries (LMIC). Prognostic models can provide standardised risk assessment to guide clinical management and can be vital to reduce and prevent maternal and perinatal mortality and morbidity. This review provides a comprehensive summary of prognostic models for adverse maternal and perinatal outcomes developed and/or validated in LMIC.MethodsA systematic search in four databases (PubMed/Medline, EMBASE, Global Health Library and The Cochrane Library) was conducted from inception (1970) up to 2 May 2018. Risk of bias was assessed with the PROBAST tool and narratively summarised.Results1741 articles were screened and 21 prognostic models identified. Seventeen models focused on maternal outcomes and four on perinatal outcomes, of which hypertensive disorders of pregnancy (n=9) and perinatal death including stillbirth (n=4) was most reported. Only one model was externally validated. Thirty different predictors were used to develop the models. Risk of bias varied across studies, with the item ‘quality of analysis’ performing the least.ConclusionPrognostic models can be easy to use, informative and low cost with great potential to improve maternal and neonatal health in LMIC settings. However, the number of prognostic models developed or validated in LMIC settings is low and mirrors the 10/90 gap in which only 10% of resources are dedicated to 90% of the global disease burden. External validation of existing models developed in both LMIC and high-income countries instead of developing new models should be encouraged.PROSPERO registration numberCRD42017058044.

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Compliance of Randomized Clinical Trials in Noncarious Cervical Lesions With the CONSORT Statement: A Systematic Review of Methodology

Operative Dentistry ◽

10.2341/17-060-l ◽

2018 ◽

Vol 43 (3) ◽

pp. E129-E151 ◽

Cited By ~ 5

Author(s):

A Reis ◽

JL de Geus ◽

L Wambier ◽

M Schroeder ◽

AD Loguercio

Keyword(s):

Clinical Trials ◽

High Risk ◽

Randomized Clinical Trials ◽

Cochrane Collaboration ◽

Consort Statement ◽

Risk Of Bias ◽

Cochrane Library ◽

Adhesive Systems ◽

Cervical Lesions ◽

Significant Difference

SUMMARY The literature was reviewed to evaluate the compliance of randomized clinical trials (RCTs) with the CONsolidated Standards of Reporting Trials (CONSORT ) and the risk of bias of these studies through the Cochrane Collaboration risk of bias tool (CCRT). RCTs were searched at Cochrane Library, PubMed, and other electronic databases to find studies about adhesive systems for cervical lesions. The compliance of the articles with CONSORT was evaluated using the following scale: 0 = no description, 1 = poor description, and 2 = adequate description. Descriptive analyses about the number of studies by journal, follow-up period, country, and quality assessments were performed with CCRT for assessing risk of bias in RCTs. One hundred thirty-eight RCTs were left for assessment. More than 30% of the studies received scores of 0 or 1. Flow chart, effect size, allocation concealment, and sample size were more critical items, with 80% receiving a score of 0. The overall CONSORT score for the included studies was 15.0 ± 4.8 points, which represents 46.9% of the maximum CONSORT score. A significant difference among countries was observed (p<0.001), as well as range of year (p<0.001). Only 4.3% of the studies were judged as at low risk; 36.2% were classified as having unclear risk and 59.4% as having high risk of bias. The adherence of RCTs evaluating adhesive systems to the CONSORT is low with unclear/high risk of bias.

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COVID-19 prevention and control measures in workplace settings: a rapid review and meta-analysis

10.31219/osf.io/vn7x6 ◽

2021 ◽

Author(s):

Carolyn Ingram ◽

Vicky Downey ◽

Mark Roe ◽

Fionn Cléirigh Büttner ◽

Yanbing Chen ◽

...

Keyword(s):

High Risk ◽

Prevention And Control ◽

Meta Analysis ◽

Control Measures ◽

Contact Tracing ◽

Rapid Review ◽

Cochrane Library ◽

Infection Prevention And Control ◽

Post Intervention ◽

And Control

Workplaces are high-risk environments for SARS-CoV-2 outbreaks and subsequent community transmission. Identifying, understanding, and implementing effective workplace SARS-CoV-2 infection prevention and control (IPC) measures is critical to protect workers, their families, and communities. A rapid review and meta-analysis were conducted to synthesize evidence assessing the effectiveness of COVID-19 IPC measures implemented in global workplace settings through April 2021. Medline, Embase, PubMed, and Cochrane Library were searched for studies that quantitatively assessed the effectiveness of workplace COVID-19 IPC measures. Included studies comprised varying empirical designs and occupational settings. Measures of interest included surveillance measures, outbreak investigations, personal protective equipment (PPE), changes in work arrangements, and worker education. Sixty-three studies from international healthcare, nursing home, meatpacking, manufacturing, and office settings were included, accounting for ~280,000 employees. Meta-analyses showed that combined measures (0.2% positivity; 95%CI 0-0.4%) were associated with lower post-intervention employee COVID-19 positivity estimates than single measures like asymptomatic PCR testing (1.7%; 95%CI 0.9-2.9%) and universal masking (24%; 95%CI 3.4-55.5%). Modelling studies showed that combinations of (i) timely and widespread contact tracing and case isolation, (ii) facilitating smaller worker cohorts, and (iii) effective use of PPE can reduce workplace transmission. Comprehensive COVID-19 IPC measures incorporating swift contact tracing and case isolation, PPE, and facility zoning, can effectively prevent workplace outbreaks. Masking alone should not be considered as sufficient protection from SARS-CoV-2 outbreaks in workplace environments at high risk of virus transmission.

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Risk Prediction Models for Liver Cancer in Adults: A Systematic Review and Meta-Analysis

10.21203/rs.3.rs-138018/v1 ◽

2021 ◽

Author(s):

Maomao Cao ◽

He Li ◽

Dianqin Sun ◽

Siyi He ◽

Yadi Zheng ◽

...

Keyword(s):

Systematic Review ◽

High Risk ◽

Liver Cancer ◽

Risk Prediction ◽

Prediction Models ◽

Meta Analysis ◽

Risk Of Bias ◽

Risk Models ◽

Risk Prediction Models ◽

Prediction Risk

Abstract Background Prediction of liver cancer risk is beneficial to define high-risk population of liver cancer and guide clinical decisions. We aimed to review and critically appraise the quality of existing risk-prediction models for liver cancer. Methods This systematic review followed the guidelines of CHARMS (Checklist for Critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) and Preferred Reporting Items for Systematic Reviews and Meta (PRISMA). We searched for PubMed, Embase, Web of Science, and the Cochrane Library from inception to July 2020. Prediction model Risk Of Bias Assessment Tool was used to assess the risk of bias of all potential articles. A narrative description and meta-analysis were conducted. Results After removal irrespective and duplicated citations, 20 risk prediction publications were finally included. Within the 20 studies, 15 studies performed model derivation and validation process, three publications only conducted developed procedure without validation and two articles were used to validate existing models. Discrimination was expressed as area under curve or C statistic, which was acceptable for most models, ranging from 0.64 to 0.96. Calibration of the predictions model were rarely assessed. All models were graded at high risk of bias. The risk bias of applicability in 13 studies was considered low. Conclusions This systematic review gives an overall review of the prediction risk models for liver cancer, pointing out several methodological issues in their development. No prediction risk models were recommended due to the high risk of bias.Systematic review registration: This systematic has been registered in PROSPERO (International Prospective Register of Systemic Review: CRD42020203244).

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