Clinical Practice Guidelines and Managing Financial Conflicts of Interest
Bibliographic record
Abstract
Abstract Clinical practice guidelines (CPGs) are becoming essential for doctors to be able to deliver evidence‐based healthcare to their patients but the organisations and committees that sponsor and write the CPGs often have financial conflicts of interest (FCOI) with pharmaceutical companies whose products are recommended in the CPG. The existence of FCOI is a concern as it may compromise the quality of the CPG. Since the main concern is whether the quality of CPGs is biased by FCOI, the next section examines the quality of guidelines and the recommendations that they make. If FCOI is a problem, as I argue, then reforms are necessary. Both the Guidelines International Network and the United States Institute of Medicine have proposed ways of dealing with FCOI and their strengths and weaknesses are explored. Finally, I propose additional measures to help CPGs achieve their potential to help clinicians. Key Concepts Clinical practice guidelines (CPGs) are becoming increasingly necessary as medical problems become more complex. Financial conflicts of interest (FCOI) are a potential threat to the integrity of CPGs. FCOI among members of committees that write CPGs, chairs of committees and organisations that sponsor CPGs is widespread. There is an association between the presence of FCOI and the quality of recommendations in CPGs. Both the Guidelines International Network and the United States Institute of Medicine (now the National Academy of Medicine) have proposed ways of dealing with FCOI. Additional reforms are necessary to ensure that CPGs are free of the bias that FCOI introduces.
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 | 0.137 | 0.558 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.020 | 0.021 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".