The Canadian Diabetes Association guidelines:
Bibliographic record
Abstract
Are clinical practice guidelines unduly influenced bythe financial associations and competing interests ofthe experts who write them? The lead editorial of our Nov. 22 issue was stimulated by a news release we had re-ceived from the Canadian Diabetes Association (CDA) that criticized the Common Drug Review (CDR) for not approv-ing for provincial formulary listing a new long-acting insulin that the CDA had recommended in its clinical-practice guidelines. According to the CDA, the CDR’s judgment was uninformed because no clinical experts were involved. In re-acting to this statement, we discovered that neither the CDA’s nor the CDR’s expert panels revealed or even dis-cussed potential financial conflicts of interest among the ex-perts who were making the recommendations. In this trio of articles, the CDA and CDR explain their guideline and recommendation processes and their ration-ales for not revealing authors ’ conflicts of interest. We also asked Dave Davis to comment more generally on the prob-lem of producing unbiased clinical practice guidelines and solutions that are being implemented to improve guideline quality.
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 | 0.037 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.017 | 0.015 |
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".