Transparency ethics in practice: Revisiting financial conflicts of interest disclosure forms in clinical practice guidelines
Bibliographic record
Abstract
BACKGROUND: Authors of clinical practice guidelines (CPGs) disclose financial conflicts of interest (FCOIs) to promote transparency ethics. Typically, they do so on standard declaration forms containing generic open-ended questions on FCOIs. Yet, the literature is scant on the format and effect of alternative disclosure forms. Does supplementing a standard form with subsequent detailed disclosure forms tailored to the context of the CPG improve the yield or accuracy of FCOIs declarations? METHODS: For an international CPG in gastroenterology on the endoscopic surveillance for colorectal neoplasia in inflammatory bowel disease, we compared the use of a standard FCOIs disclosure form with a contextual FCOIs disclosure form that detailed commercial relations related to the CPG topic. This included manufacturers of endoscopes, endoscopy equipment and accessories. Participants completed the generic form early, and the supplementary contextual form six months later. We then compared the FCOI disclosures obtained. FINDINGS: 26 participants provided FCOIs disclosures using both disclosure forms. We found discrepancies regarding (1) the disclosure of FCOIs (presence/absence), and (2) the listing of financial entities. While the number of participants who disclosed a FCOI remained the same (30.8%) using the two forms, disclosures were not from the same individuals: two additional participants disclosed a FCOI, whereas two participants withdrew previous disclosures. Among those who reported a FCOI in either form, we noted inconsistencies in disclosures for 70% of the participants. This included changes in FCOIs disclosure status or modifications of "their commercial relations". DISCUSSION: Accurate reporting of FCOIs advances the transparency and ethical integrity of CPGs. Our experience suggests that a contextual FCOIs disclosure form tailored to content of the CPG with narrow, detailed questions provides supplementary, more complete FCOIs declarations than generic forms alone. The finding raises challenges on how forms are best written and formatted, optimally timed, and more effectively processed with sensitivity to professional behaviour, so as to heighten transparency.
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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 | 0.711 | 0.928 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.032 | 0.048 |
| Open science | 0.012 | 0.026 |
| Research integrity | 0.016 | 0.028 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier 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".