Replication of the Weber Effect Using Postmarketing Adverse Event Reports Voluntarily Submitted to the United States Food and Drug Administration
Bibliographic record
Abstract
STUDY OBJECTIVE: To validate or refute a widely accepted epidemiologic phenomenon known as the Weber effect by replicating Weber's original observation by using drugs that were marketed in the United States and using reports from a U.S. database. DESIGN: Retrospective analysis of adverse event databases. SETTING: University research center. DRUGS: The original nonsteroidal antiinflammatory drugs studied by Weber that were approved by the U.S. Food and Drug Administration (FDA) and marketed in the United States: diclofenac sodium, diclofenac potassium, diflunisal, sulindac, flurbiprofen, and piroxicam. INTERVENTION: Reports of adverse events submitted to the FDAs Spontaneous Reporting System and the Adverse Event Reporting System from January 1969-December 2000 for these drugs were analyzed according to the number of adverse events reported for each drug per year from the time the drug was approved until December 2000. MEASUREMENTS AND MAIN RESULTS: Reporting patterns were considered to demonstrate the Weber effect if the highest peak in reports during the first 5 years after product approval occurred during year 2. All five drugs analyzed in this study demonstrated the Weber effect. CONCLUSION: The Weber effect was replicable by using drugs marketed in the United States and using reports that were submitted to a U.S. database. Various other factors affected spontaneous reporting of adverse events, as peaks in the number of reports were seen numerous times for each drug after the initial 5-year marketing period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.175 | 0.412 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".