A180 PREVALENCE AND RELEVANCE OF FINANCIAL CONFLICTS OF INTEREST AMONG CLINICAL PRACTICE GUIDELINES IN NUTRITION
Bibliographic record
Abstract
There is broadly documented evidence that industry funding can influence clinical practice guidelines (CPGs). This is seen in many drug-industry sponsored research but the role in nutrition science literature is unclear. We reviewed the prevalence and relevance of financial conflicts of interest (FCOI) in CPGs published by the top global nutrition societies. Our aim is to improve the current FCOI disclosure practices in nutrition CPG development. We performed a cross-sectional analysis of FCOIs declared by nutrition CPG authors. We assessed the prevalence of declared and undeclared FCOI by guideline authorship and determined relevancy of sponsorship to the guideline content. We assessed adherence to the National Academy of Medicine (NAM) practice standard which states CPG development and funding should be explicitly stated and publicly available. We identified 19 guidelines with 195 authors. Analysis revealed 8 (42%) guidelines with stated disclosures, half of which were explicitly declared in the CPG and the other half whereby declarations were inaccessible to the public. In respect of those guidelines whereby disclosed and undisclosed FCOI could be assessed, 100% of the stated disclosures were relevant to the authored guideline and 66% of undisclosed relationships were relevant. There is some evidence of undisclosed FCOI where studies state there were none to declare. This data suggests that almost half of the recent nutrition CPGs reviewed do not adhere to NAM transparency standards. Many authors of recently published nutrition CPGs have relevant FCOI, both disclosed and undisclosed. We have determined that transparency could be improved to achieve compliance with the NAM standards and mitigate potential for bias that could affect research integrity. None
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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 | 0.112 | 0.532 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".