Assessing cumulative effects of medication use: New insights and new challenges
Bibliographic record
Abstract
Key Points• Weighted cumulative exposure (WCE) allows an accurate assessment of cumulative effects of time-varying drug exposures, while accounting for their recency.• Real-world pharmacoepidemiological applications demonstrate that the WCE approach often improves the model's fit to data, and may be essential to establish an association between the use of a drug and a health outcome.• Flexible cubic spline estimates of the weight function may yield new insights regarding how the hazard varies with patterns of past drug use, including variations in dosage, duration and recency of treatment.• Kelly et al. identify some outstanding analytical issues that should be addressed in future extensions of the WCE methodology.• For future refinements of WCE modeling, it is important to assess, and ultimately to try to correct for, the impact of typical limitations of
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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 | 0.103 | 0.182 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.015 |
| 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".