Response to the Letter to the Editor: “Examining Bias and Reporting in Oral Health Prediction Modeling Studies”

Recent efforts to improve the reliability and efficiency of scientific research have caught the attention of researchers conducting prediction modeling studies (PMSs). Use of prediction models in oral health has become more common over the past decades for predicting the risk of diseases and treatment outcomes. Risk of bias and insufficient reporting present challenges to the reproducibility and implementation of these models. A recent tool for bias assessment and a reporting guideline—PROBAST (Prediction Model Risk of Bias Assessment Tool) and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis)—have been proposed to guide researchers in the development and reporting of PMSs, but their application has been limited. Following the standards proposed in these tools and a systematic review approach, a literature search was carried out in PubMed to identify oral health PMSs published in dental, epidemiologic, and biostatistical journals. Risk of bias and transparency of reporting were assessed with PROBAST and TRIPOD. Among 2,881 papers identified, 34 studies containing 58 models were included. The most investigated outcomes were periodontal diseases (42%) and oral cancers (30%). Seventy-five percent of the studies were susceptible to at least 4 of 20 sources of bias, including measurement error in predictors ( n = 12) and/or outcome ( n = 7), omitting samples with missing data ( n = 10), selecting variables based on univariate analyses ( n = 9), overfitting ( n = 13), and lack of model performance assessment ( n = 24). Based on TRIPOD, at least 5 of 31 items were inadequately reported in 95% of the studies. These items included sampling approaches ( n = 15), participant eligibility criteria ( n = 6), and model-building procedures ( n = 16). There was a general lack of transparent reporting and identification of bias across the studies. Application of the recommendations proposed in PROBAST and TRIPOD can benefit future research and improve the reproducibility and applicability of prediction models in oral health.

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Response to Letter to the Editor, “Clinical Trial Registration in Oral Health Journals”

Journal of Dental Research ◽

10.1177/0022034514563955 ◽

2014 ◽

Vol 94 (3_suppl) ◽

pp. 104S-104S

Author(s):

V. Smaïl-Faugeron ◽

H. Fron-Chabouis ◽

P. Durieux

Keyword(s):

Clinical Trial ◽

Oral Health ◽

Trial Registration ◽

Clinical Trial Registration ◽

Letter To The Editor

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Heavy metal uptake by water lettuce (Pistia stratiotes L.) from paper mill effluent (PME): experimental and prediction modeling studies

Environmental Science and Pollution Research ◽

10.1007/s11356-019-04766-2 ◽

2019 ◽

Vol 26 (14) ◽

pp. 14400-14413 ◽

Cited By ~ 9

Author(s):

Vinod Kumar ◽

Jogendra Singh ◽

Pankaj Kumar

Keyword(s):

Heavy Metal ◽

Metal Uptake ◽

Paper Mill ◽

Pistia Stratiotes ◽

Water Lettuce ◽

Heavy Metal Uptake ◽

Prediction Modeling ◽

Paper Mill Effluent ◽

Modeling Studies

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Prediction Modeling Studies for Medical Usage Rates in Mass Gatherings: A Systematic Review

Prehospital and Disaster Medicine ◽

10.1017/s1049023x19000979 ◽

2019 ◽

Vol 34 (s1) ◽

pp. s40-s40

Author(s):

Hans Van Remoortel ◽

Hans Scheers ◽

Emmy De Buck ◽

Karen Lauwers ◽

Philippe Vandekerckhove

Keyword(s):

Systematic Review ◽

Regression Model ◽

Free Water ◽

Dew Point ◽

Statistical Regression ◽

Prediction Modeling ◽

Mass Gatherings ◽

Modeling Studies ◽

Usage Rates

Introduction:Mass gatherings attended by large crowds are an increasingly common feature of society. In parallel, an increased number of studies have been conducted to identify those variables that are associated with increased medical usage rates.Aim:To identify studies that developed and/or validated a statistical regression model predicting patient presentation rate (PPR) or transfer to hospital rate (TTHR) at mass gatherings.Methods:Prediction modeling studies from 6 databases were retained following systematic searching. Predictors for PPR and/or TTHR that were included in a multivariate regression model were selected for analysis. The GRADE methodology (Grades of Recommendation, Assessment, Development, and Evaluation) was used to assess the quality of evidence.Results:We identified 11 prediction modeling studies with a combined audience of >32 million people in >1500 mass gatherings. Eight cross-sectional studies developed a prediction model in a mixed audience of (spectator) sports events, music concerts, and public exhibitions. Statistically significant variables (p<0.05) to predict PPR and/or TTHR were as follows: accommodation (seated, boundaries, indoor/outdoor, maximum capacity, venue access), type of event, weather conditions (humidity, dew point, heat index), crowd size, day vs night, demographic variables (age/gender), sports event distance, level of competition, free water availability, and specific TTHR-predictive factors (injury status: number of patient presentations, type of injury). The quality of the evidence was considered as low. Three studies externally validated their model against existing models. Two validation studies showed a large underestimation of the predicted patients presentations or transports to hospital (67-81%) whereas one study overestimated these outcomes by 10-28%.Discussion:This systematic review identified a comprehensive list of relevant predictors which should be measured to develop and validate future models to predict medical usage at mass gatherings. This will further scientifically underpin more effective pre-event planning and resource provision.

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