Selecting Risk of Bias Tools for Observational Studies for a Systematic Review of Anthropometric Measurements and Dental Caries among Children
Bibliographic record
Abstract
In conducting a systematic review, assessing the risk of bias of the included studies is a vital step; thus, choosing the most pertinent risk of bias (ROB) tools is crucial. This paper determined the most appropriate ROB tools for assessing observational studies in a systematic review assessing the association between anthropometric measurements and dental caries among children. First, we determined the ROB tools used in previous reviews on a similar topic. Subsequently, we reviewed articles on ROB tools to identify the most recommended ROB tools for observational studies. Of the twelve ROB tools identified from the previous steps, three ROB tools that best fit the eight criteria of a good ROB tool were the Newcastle-Ottawa Scale (NOS) for cohort and case-control studies, and Agency for Healthcare Research and Quality (AHRQ) and the Effective Public Health Practice Project (EPHPP) for a cross-sectional study. We further assessed the inter-rater reliability for all three tools by analysing the percentage agreement, inter-class correlation coefficient (ICC) and kappa score. The overall percentage agreements and reliability scores of these tools ranged from good to excellent. Two ROB tools for the cross-sectional study were further evaluated qualitatively against nine of a tool's advantages and disadvantages. Finally, the AHRQ and NOS were selected as the most appropriate ROB tool to assess cross-sectional and cohort studies in the present review.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.319 | 0.677 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.019 | 0.027 |
| Bibliometrics | 0.047 | 0.029 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".