Competing interests in development of clinical practice guidelines for diabetes management: Report from a multidisciplinary workshop
Bibliographic record
Abstract
OBJECTIVE: To explore the complex issue of competing interests (CIs) in development of clinical practice guidelines (CPGs) in diabetes with stakeholders. METHODS: A multidisciplinary panel of 26 health, methodological, legal, and bioethical experts, trainees, and lay people from across Canada participated in a workshop on CIs in CPGs. Mixed methods were used such that qualitative themes were extracted from the discussions and quantitative survey data were collected. RESULTS: In the discussions, participants acknowledged that potential competing interests were not uncommon among sponsoring organizations and authors of CPGs. Avoidance of all potential CIs in development of CPGs was emulated as ideal, but considered probably unrealistic, given the paucity of peer-reviewed funding opportunities for development of evidence-informed CPGs and the scarcity of knowledgeable authors without CIs. An optimal approach for management of CIs in CPGs could not be agreed upon by participants. Full disclosure of any financial CIs for authors and sponsoring organizations as well as discouragement of external financial contributors from writing involvement, were endorsed by participants in the workshop and a subsequent survey. CONCLUSIONS: Complete disclosure of financial CIs of sponsoring organizations and authors of CPGs is essential, yet the optimal approach to management of potential CIs is currently undefined.
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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 | 0.102 | 0.261 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".