The Vexing Problem of Guidelines and Conflict of Interest: A Potential Solution
Bibliographic record
Abstract
Issues of financial and intellectual conflict of interest in clinical practice guidelines have raised increasing concern. Professional organizations have responded by more rigorous regulation of conflict of interest. Nevertheless, tension remains between the competing goals of optimizing guideline quality by using the experience and insight of experts and ensuring that financial and intellectual conflicts of interest do not influence recommendations. The executive committee of the American College of Chest Physicians' Antithrombotic Guidelines has developed a strategy comprising 3 innovative aspects to address this tension: First, place equal emphasis on intellectual and financial conflicts and provide explicit criteria for both; second, a methodologist without important conflicts of interest should have primary responsibility for each chapter; and third, experts with important financial or intellectual conflicts of interest can collect and interpret evidence, but only panel members without important conflicts can be involved in developing the recommendation for a specific question. These strategies may help to achieve the benefits of expert input without conflicts of interest influencing recommendations.
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 | 0.274 | 0.551 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.016 | 0.038 |
| Open science | 0.010 | 0.018 |
| Research integrity | 0.049 | 0.047 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".