scholarly journals Protocol for Development of a Reporting Guideline for Causal and Counterfactual Prediction Models

2021 ◽  
Author(s):  
Jie Xu ◽  
Yi Guo ◽  
Fei Wang ◽  
Hua Xu ◽  
Robert Lucero ◽  
...  
Keyword(s):  
Systematic Review ◽  
Prediction Models ◽  
Reporting Guideline ◽  
Randomized Experiments ◽  
Biomedical Sciences ◽  
Model Studies ◽  
Stage 4 ◽  
Computer Based ◽  
Prediction Modelling ◽  
Stage 1

[Introduction] While there are protocols for reporting on observational studies (e.g., STROBE, RECORD), estimation of causal effects from both observational data and randomized experiments (e.g., AGREMA, CONSORT), and on prediction modelling(e.g., TRIPOD), none is purposely made for assessing the ability and reliability of models to predict counterfactuals for individuals upon one or more possible interventions, on the basis of given (or inferred) causal structures. This paper describes methods and processes that will be used to develop a reporting guideline for causal and counterfactual prediction models(tentative acronym: PRECOG). [Materials and Methods] PRECOG will be developed following published guidance from the EQUATOR network, and will comprise five stages. Stage 1 will be bi-weekly meetings of a working group with external advisors (active until stage 5). Stage 2 will comprise a scoping/systematic review of literature on counterfactual prediction modelling for biomedical sciences(registered in PROSPERO). In stage 3, we will perform a computer-based, real-time Delphi survey to consolidate the PRECOGchecklist, involving experts in causal inference, statistics, machine learning, prediction modelling and protocols/standards. Stage 4 will involve the write-up of the PRECOG guideline (including its checklist) based on the results from the prior stages. In stage 5, we will work on the publication of the guideline and of the scoping/systematic review as peer-reviewed, open-access papers, and on their dissemination through conferences, websites, and social media. [Conclusions] PRECOG can help researchers and policymakers to carry out and critically appraise causal and counterfactual prediction model studies. PRECOG will also be useful for designing interventions, and we anticipate further expansion of the guideline for specific areas, e.g., pharmaceutical interventions.

scholarly journals Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques

BMJ Open ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. e038832
Author(s):  
Constanza L Andaur Navarro ◽  
Johanna A A G Damen ◽  
Toshihiko Takada ◽  
Steven W J Nijman ◽  
Paula Dhiman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Prediction Model ◽  
Prediction Models ◽  
Reporting Quality ◽  
Risk Of Bias ◽  
Machine Learning Techniques ◽  
Published Data ◽  
Model Studies

IntroductionStudies addressing the development and/or validation of diagnostic and prognostic prediction models are abundant in most clinical domains. Systematic reviews have shown that the methodological and reporting quality of prediction model studies is suboptimal. Due to the increasing availability of larger, routinely collected and complex medical data, and the rising application of Artificial Intelligence (AI) or machine learning (ML) techniques, the number of prediction model studies is expected to increase even further. Prediction models developed using AI or ML techniques are often labelled as a ‘black box’ and little is known about their methodological and reporting quality. Therefore, this comprehensive systematic review aims to evaluate the reporting quality, the methodological conduct, and the risk of bias of prediction model studies that applied ML techniques for model development and/or validation.Methods and analysisA search will be performed in PubMed to identify studies developing and/or validating prediction models using any ML methodology and across all medical fields. Studies will be included if they were published between January 2018 and December 2019, predict patient-related outcomes, use any study design or data source, and available in English. Screening of search results and data extraction from included articles will be performed by two independent reviewers. The primary outcomes of this systematic review are: (1) the adherence of ML-based prediction model studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD), and (2) the risk of bias in such studies as assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). A narrative synthesis will be conducted for all included studies. Findings will be stratified by study type, medical field and prevalent ML methods, and will inform necessary extensions or updates of TRIPOD and PROBAST to better address prediction model studies that used AI or ML techniques.Ethics and disseminationEthical approval is not required for this study because only available published data will be analysed. Findings will be disseminated through peer-reviewed publications and scientific conferences.Systematic review registrationPROSPERO, CRD42019161764.

scholarly journals Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Constanza L. Andaur Navarro ◽  
Johanna A. A. Damen ◽  
Toshihiko Takada ◽  
Steven W. J. Nijman ◽  
Paula Dhiman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Prediction Model ◽  
Prediction Models ◽  
Supervised Machine Learning ◽  
Model Specification ◽  
Essential Information ◽  
Model Studies ◽  
Completeness Of Reporting ◽  
Complete Reporting

Abstract Background While many studies have consistently found incomplete reporting of regression-based prediction model studies, evidence is lacking for machine learning-based prediction model studies. We aim to systematically review the adherence of Machine Learning (ML)-based prediction model studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement. Methods We included articles reporting on development or external validation of a multivariable prediction model (either diagnostic or prognostic) developed using supervised ML for individualized predictions across all medical fields. We searched PubMed from 1 January 2018 to 31 December 2019. Data extraction was performed using the 22-item checklist for reporting of prediction model studies (www.TRIPOD-statement.org). We measured the overall adherence per article and per TRIPOD item. Results Our search identified 24,814 articles, of which 152 articles were included: 94 (61.8%) prognostic and 58 (38.2%) diagnostic prediction model studies. Overall, articles adhered to a median of 38.7% (IQR 31.0–46.4%) of TRIPOD items. No article fully adhered to complete reporting of the abstract and very few reported the flow of participants (3.9%, 95% CI 1.8 to 8.3), appropriate title (4.6%, 95% CI 2.2 to 9.2), blinding of predictors (4.6%, 95% CI 2.2 to 9.2), model specification (5.2%, 95% CI 2.4 to 10.8), and model’s predictive performance (5.9%, 95% CI 3.1 to 10.9). There was often complete reporting of source of data (98.0%, 95% CI 94.4 to 99.3) and interpretation of the results (94.7%, 95% CI 90.0 to 97.3). Conclusion Similar to prediction model studies developed using conventional regression-based techniques, the completeness of reporting is poor. Essential information to decide to use the model (i.e. model specification and its performance) is rarely reported. However, some items and sub-items of TRIPOD might be less suitable for ML-based prediction model studies and thus, TRIPOD requires extensions. Overall, there is an urgent need to improve the reporting quality and usability of research to avoid research waste. Systematic review registration PROSPERO, CRD42019161764.

Alterations in the number of motoneurons containing immunoreactive calcitonin gene-related peptide(CGRP)& choline acetyltransferase(ChAT) in the cervical spinal cord of the wobbler mouse during the development of the motoneuron disease

1995 ◽  
Vol 53 ◽  
pp. 970-971
Author(s):  
L. Vacca-Galloway ◽  
Y.Q. Zhang ◽  
P. Bose ◽  
S.H. Zhang
Keyword(s):  
Spinal Cord ◽  
Early Stage ◽  
Cervical Spinal Cord ◽  
Wild Type Mouse ◽  
Reflex Response ◽  
Mouse Strains ◽  
Behavioral Tests ◽  
Wobbler Mouse ◽  
Stage 4 ◽  
Stage 1

The Wobbler mouse (wr) has been studied as a model for inherited human motoneuron diseases (MNDs). Using behavioral tests for forelimb power, walking, climbing, and the “clasp-like reflex” response, the progress of the MND can be categorized into early (Stage 1, age 21 days) and late (Stage 4, age 3 months) stages. Age-and sex-matched normal phenotype littermates (NFR/wr) were used as controls (Stage 0), as well as mice from two related wild-type mouse strains: NFR/N and a C57BI/6N. Using behavioral tests, we also detected pre-symptomatic Wobblers at postnatal ages 7 and 14 days. The mice were anesthetized and perfusion-fixed for immunocytochemical (ICC) of CGRP and ChAT in the spinal cord (C3 to C5).Using computerized morphomety (Vidas, Zeiss), the numbers of IR-CGRP labelled motoneurons were significantly lower in 14 day old Wobbler specimens compared with the controls (Fig. 1). The same trend was observed at 21 days (Stage 1) and 3 months (Stage 4). The IR-CGRP-containing motoneurons in the Wobbler specimens declined progressively with age.

How well do voice-hearing assessment measures capture the positive experiences of individuals?

2019 ◽  
Author(s):  
Lucy Armstrong ◽  
Lorna Hogg ◽  
Pamela Charlotte Jacobsen
Keyword(s):  
Systematic Review ◽  
Lived Experience ◽  
Second Stage ◽  
The Third ◽  
Third Stage ◽  
Hearing Assessment ◽  
Positive Experiences ◽  
Assessment Measures ◽  
Stage 1 ◽  
The Voice

The first stage of this project aims to identify assessment measures which include items on voice-hearing by way of a systematic review. The second stage is the development of a brief framework of categories of positive experiences of voice hearing, using a triangulated approach, drawing on views from both professionals and people with lived experience. The third stage will involve using the framework to identify any positve aspects of voice-hearing included in the voice hearing assessments identified in stage 1.

Novel staging classification of primary mitral regurgitation based on the presence of cardiac damage

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
A.L Van Wijngaarden ◽  
Y.L Hiemstra ◽  
P Van Der Bijl ◽  
V Delgado ◽  
N Ajmone Marsan ◽  
...  
Keyword(s):  
Mitral Regurgitation ◽  
Kidney Function ◽  
Left Ventricular ◽  
Prognostic Impact ◽  
Cardiac Damage ◽  
Stage 4 ◽  
All Cause Mortality ◽  
Primary Mitral Regurgitation ◽  
Stage 1 ◽  
Staging Classification

Abstract Background The indication for surgery in patients with severe primary mitral regurgitation (MR) is currently based on the presence of symptoms, left ventricular (LV) dilatation and dysfunction, atrial fibrillation and pulmonary hypertension. The aim of this study was to evaluate the prognostic impact of a new staging classification based on cardiac damage including the known risk factors but also including global longitudinal strain (GLS), severe left atrial (LA) dilatation and right ventricular (RV) dysfunction. Methods In total 614 patients who underwent surgery for severe primary MR with available baseline transthoracic echocardiograms were included. Patients were classified according to the extent of cardiac damage (Figure): Stage 0-no cardiac damage, Stage 1-LV damage, Stage 2-LA damage, Stage 3-pulmonary vasculature or tricuspid valve damage and Stage 4-RV damage. Patients were followed for all-cause mortality. Results Based on the proposed classification, 172 (28%) patients were classified as Stage 0, 102 (17%) as Stage 1, 134 (21%) as Stage 2, 135 (22%) as Stage 3 and 71 (11%) as Stage 4. The more advanced the stage, the older the patients were with worse kidney function, more symptoms and higher EuroScore. Kaplan-Meier curve analysis revealed that patients with more advanced stages of cardiac damage had a significantly worse survival (log-rank chi-square 35.2; p<0.001) (Figure). On multivariable analysis, age, male, chronic obstructive pulmonary disease, kidney function, and stage of cardiac damage were independently associated with all-cause mortality. For each stage increase, a 22% higher risk for all-cause mortality was observed (95% CI: 1.064–1.395; p=0.004). Conclusion In patients with severe primary MR, a novel staging classification based on the extent of cardiac damage, may help refining risk stratification, particularly including also GLS, LA dilatation and RV dysfunction in the assessment. Funding Acknowledgement Type of funding source: None

scholarly journals How effective are electronic medication systems in reducing medication error rates and associated harm among hospital inpatients? A systematic review and meta-analysis

2020 ◽  
Author(s):  
Peter J Gates ◽  
Rae-Anne Hardie ◽  
Magdalena Z Raban ◽  
Ling Li ◽  
Johanna I Westbrook
Keyword(s):  
Systematic Review ◽  
Medication Error ◽  
Medication Errors ◽  
Meta Analysis ◽  
Error Rates ◽  
Prescribing Error ◽  
Before And After ◽  
Actual Harm ◽  
Computer Based ◽  
Wide Scale

Abstract Objective To conduct a systematic review and meta-analysis to assess: 1) changes in medication error rates and associated patient harm following electronic medication system (EMS) implementation; and 2) evidence of system-related medication errors facilitated by the use of an EMS. Materials and Methods We searched Medline, Scopus, Embase, and CINAHL for studies published between January 2005 and March 2019, comparing medication errors rates with or without assessments of related harm (actual or potential) before and after EMS implementation. EMS was defined as a computer-based system enabling the prescribing, supply, and/or administration of medicines. Study quality was assessed. Results There was substantial heterogeneity in outcomes of the 18 included studies. Only 2 were strong quality. Meta-analysis of 5 studies reporting change in actual harm post-EMS showed no reduced risk (RR: 1.22, 95% CI: 0.18–8.38, P = .8) and meta-analysis of 3 studies reporting change in administration errors found a significant reduction in error rates (RR: 0.77, 95% CI: 0.72–0.83, P = .004). Of 10 studies of prescribing error rates, 9 reported a reduction but variable denominators precluded meta-analysis. Twelve studies provided specific examples of system-related medication errors; 5 quantified their occurrence. Discussion and Conclusion Despite the wide-scale adoption of EMS in hospitals around the world, the quality of evidence about their effectiveness in medication error and associated harm reduction is variable. Some confidence can be placed in the ability of systems to reduce prescribing error rates. However, much is still unknown about mechanisms which may be most effective in improving medication safety and design features which facilitate new error risks.

A Description of Middle School Male Singers’ Voice Change and Voice Part Assignment

2021 ◽  
pp. 875512332110182
Author(s):  
Ryan Austin Fisher ◽  
Nancy L. Summitt ◽  
Ellen B. Koziel
Keyword(s):  
Middle School ◽  
Sixth Grade ◽  
Eighth Grade ◽  
Voice Change ◽  
Stage 4 ◽  
Public Middle School ◽  
Middle School Choir ◽  
Choral Programs ◽  
Stage 1 ◽  
The Voice

The purpose of this study was to describe the voice change and voice part assignment of male middle school choir members. Volunteers ( N = 92) were recruited from three public middle school choral programs (Grades 6-8). Participants were audio-recorded performing simple vocal tasks in order to assess vocal range and asked to share the music they were currently singing in class. Results revealed 23.91% of participants’ voices could be categorized as unchanged, 14.13% as Stage 1, 3.26% as Stage 2, 10.87% as Stage 3, 26.09% as Stage 4, and 21.74% as Stage 5. The majority of sixth-grade participants were classified as unchanged or in Stage 1 of the voice change and the majority of eighth-grade participants were classified in Stages 4 to 5 of the voice change. Of the participants labeled “tenors” in their choir, over 60% were classified as either unchanged voices or in Stage 1 of the voice change.

scholarly journals Temporal changes of quantitative CT findings from 102 patients with COVID-19 in Wuhan, China: A longitudinal study

2021 ◽  
Vol 29 ◽  
pp. 297-309
Author(s):  
Xiaohui Chen ◽  
Wenbo Sun ◽  
Dan Xu ◽  
Jiaojiao Ma ◽  
Feng Xiao ◽  
...  
Keyword(s):  
Lung Diseases ◽  
Early Stage ◽  
Temporal Changes ◽  
Time Interval ◽  
Lesion Area ◽  
Quantitative Ct ◽  
Diagnosis And Prognosis ◽  
Ct Findings ◽  
Stage 4 ◽  
Stage 1

BACKGROUND: Computed tomography (CT) imaging combined with artificial intelligence is important in the diagnosis and prognosis of lung diseases. OBJECTIVE: This study aimed to investigate temporal changes of quantitative CT findings in patients with COVID-19 in three clinic types, including moderate, severe, and non-survivors, and to predict severe cases in the early stage from the results. METHODS: One hundred and two patients with confirmed COVID-19 were included in this study. Based on the time interval between onset of symptoms and the CT scan, four stages were defined in this study: Stage-1 (0 ∼7 days); Stage-2 (8 ∼ 14 days); Stage-3 (15 ∼ 21days); Stage-4 (> 21 days). Eight parameters, the infection volume and percentage of the whole lung in four different Hounsfield (HU) ranges, ((-, -750), [-750, -300), [-300, 50) and [50, +)), were calculated and compared between different groups. RESULTS: The infection volume and percentage of four HU ranges peaked in Stage-2. The highest proportion of HU [-750, 50) was found in the infected regions in non-survivors among three groups. CONCLUSIONS: The findings indicate rapid deterioration in the first week since the onset of symptoms in non-survivors. Higher proportion of HU [-750, 50) in the lesion area might be a potential bio-marker for poor prognosis in patients with COVID-19.

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