Vioxx redux – or how I learned to worry about industry-sponsored clinical trials
Bibliographic record
Abstract
I read with interest Mark Wilson's recent article, "The New England Journal of Medicine: commercial conflict of interest and revisiting the Vioxx scandal". I believe this is an important contribution that underlines the aphorism "Those who don't know history are doomed to repeat it." As Vioxx is a seminal example, it is important to place it in its proper context, examining if this malfeasance extends beyond the VIGOR study. While the epicentre of this conflict of interest surely begins with the sponsor, I believe the following essay demonstrates that this wave of egregiously unethical behaviour can exist and be propagated only with the complicity of academic investigators, medical journals, a flawed peer-review system and an uncritical medical readership. Perhaps the most troubling is that the factors that coalesced into the Vioxx scandal are, if anything, more ubiquitous today, mandating increased vigilance to decrease the probability of "getting fooled" again.
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 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 | 0.142 | 0.530 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.017 | 0.070 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads 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".