Judging the quality of evidence in reviews of prognostic factor research: adapting the GRADE framework
Bibliographic record
Abstract
BACKGROUND: Prognosis research aims to identify factors associated with the course of health conditions. It is often challenging to judge the overall quality of research evidence in systematic reviews about prognosis due to the nature of the primary studies. Standards aimed at improving the quality of primary studies on the prognosis of health conditions have been created, but these standards are often not adequately followed causing confusion about how to judge the evidence. METHODS: This article presents a proposed adaptation of Grading of Recommendations Assessment, Development and Evaluation (GRADE), which was developed to rate the quality of evidence in intervention research, to judge the quality of prognostic evidence. RESULTS: We propose modifications to the GRADE framework for use in prognosis research along with illustrative examples from an ongoing systematic review in the pediatric pain literature. We propose six factors that can decrease the quality of evidence (phase of investigation, study limitations, inconsistency, indirectness, imprecision, publication bias) and two factors that can increase it (moderate or large effect size, exposure-response gradient). CONCLUSIONS: We describe criteria for evaluating the potential impact of each of these factors on the quality of evidence when conducting a review including a narrative synthesis or a meta-analysis. These recommendations require further investigation and testing.
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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 | 0.689 | 0.864 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.065 | 0.030 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.016 | 0.017 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".