Authors’ reply to commentary: Renewed controversy over cardiovascular risk with non-steroidal anti-inflammatory drugs
Bibliographic record
Abstract
We wish to emphasize three issues concerning our study: its quality, its reporting and its transparency. We would encourage those interested in objectively assessing its quality to review not only the original 13-page publication, but also the 54 pages of supplementary material freely available on the BMJ website [http://www.bmj.com/content/357/bmj.j1909], to form their own conclusions.1 Assisting in this assessment are the multiple rounds of peer review that the paper underwent, including our thorough replies, again openly available [http://www.bmj.com/content/357/bmj.j1909/peer-review]. The BMJ reporting format also encourages online commentaries, which are of unrestricted length. We received 22 for our publication and, as indicated, some were accompanied by a direct reply from us. In this light, we are surprised that Stehlik et al.2 chose to publish their commentary in a different journal.2 Our main concern with their commentary is its narrow perspective and cherry-picking of isolated comments during the early stages of a long and rigorous peer review process. One of the main issues raised in their commentary is the choice of studies to be included in a meta-analysis. For clinical decision making, we require well-executed studies devoid of biases. Why then would we be satisfied with meta-analyses that include studies in which either exposure or time is manifestly misclassified? If there is an interesting aspect of this ‘renewed controversy’, it is that of the validity of meta-analytical studies that include low-quality primary studies.
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 | 0.020 | 0.188 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.068 | 0.067 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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".