Open Access Discrepancy Among Observational Studies: Example of Naproxen- Associated Adverse Events
Bibliographic record
Abstract
Abstract: Background: Observational studies assessing the cardiovascular adverse effect of naproxen have had conflicting results. It is not clear whether variation in population characteristics between studies may explain some of this discrepancy. Objective: To determine whether changes in patient characteristics of naproxen users occurred between 1999 and 2004 in Québec, Canada and to examine whether these temporal changes were accompanied by changes in estimates of naproxenrelated hospitalizations for gastrointestinal (GI) ulcers and myocardial infarction, using provincial health services administrative databases. Methods: Demographic, pharmaceutical and physician billing records of patients 65 years and older, who received naproxen or acetaminophen prescriptions between 1999 and 2004 were used. Two identical cohort studies, labeled Study 1 and Study 2 were conducted and their results were compared. One study was confined to the time period 1999-2001 and the other to 2002-2004. Patient characteristics at index date (the date of the first naproxen or acetaminophen prescription during the corresponding period) were compared between the study cohorts in naproxen and acetaminophen users, respectively, and within each study cohort between naproxen and acetaminophen users, using logistic regression models. Cox regression models with time dependent exposure were used to assess the association between naproxen vs acetaminophen and hospitalizations for GI events or AMI, respectively within each study. Results were then compared between the two studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".