New red meat guidelines are undermined by undisclosed ties and faulty methods, say critics
Bibliographic record
Abstract
New guidelines on red and processed meat1 that cast doubt on the health benefits of reducing consumption have come under fire from critics who note that the lead author of the principal paper also helped to write a 2016 paper questioning the benefits of limiting sugar intake,2 which was funded by an industry group. Bradley Johnston, an epidemiologist at Dalhousie University in Canada, did not disclose this previous funding in the conflict of interest declaration for the new red meat guidelines, which contradict most current expert advice in not recommending reduced consumption. The meat and sugar guidelines were both published in the Annals of Internal Medicine . Johnston told The BMJ that, although the sugar research paper was published in late 2016, the funding was received in 2015, and hence he did not refer to it in the journal’s conflict of interest questionnaire for the new guidelines, which asked about financial conflicts from …
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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 | 0.170 | 0.490 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.023 | 0.047 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".