Disease risk score as a confounder summary method: systematic review and recommendations
Bibliographic record
Abstract
PURPOSE: To systematically examine trends and applications of the disease risk score (DRS) as a confounder summary method. METHODS: We completed a systematic search of MEDLINE and Web of Science® to identify all English language articles that applied DRS methods. We tabulated the number of publications by year and type (empirical application, methodological contribution, or review paper) and summarized methods used in empirical applications overall and by publication year (<2000, ≥2000). RESULTS: Of 714 unique articles identified, 97 examined DRS methods and 86 were empirical applications. We observed a bimodal distribution in the number of publications over time, with a peak 1979-1980, and resurgence since 2000. The majority of applications with methodological detail derived DRS using logistic regression (47%), used DRS as a categorical variable in regression (93%), and applied DRS in a non-experimental cohort (47%) or case-control (42%) study. Few studies examined effect modification by outcome risk (23%). CONCLUSION: Use of DRS methods has increased yet remains low. Comparative effectiveness research may benefit from more DRS applications, particularly to examine effect modification by outcome risk. Standardized terminology may facilitate identification, application, and comprehension of DRS methods. More research is needed to support the application of DRS methods, particularly in case-control studies.
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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 | 0.116 | 0.344 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".