Bibliographic record
Abstract
STUDY DESIGN: Point/counterpoint. OBJECTIVE: To facilitate debate regarding conflicts of interest and ethical considerations in physician/industry relationships. SUMMARY OF BACKGROUND DATA: We find ourselves working in a health care system in which nearly $2 trillion are spent, only to rank 37th in world health. That much money at stake piques peoples' interest, and various parties, without loyalty to the Hippocratic principles, will find opportunities to gain financially. Clearly, current health expenditures do not always coincide with better outcomes. Yet some believe that over 20% of the gross national product will be necessary to maintain our health care system in the future. Medical liability causes a paradoxical situation: physicians are at times driven to use the newest technology and perform many diagnostic tests, for fear of litigation. This practice is often independent of the best evidence. METHODS: Literature search and experience. RESULTS: The nature or magnitude of conflict(s) of interest influences the risk of bias in research. It is important to acknowledge and disclose these potential conflicts while recognizing that the existence of such conflict does not necessarily adversely affect the quality of the research. CONCLUSIONS: Conflict of interest need not be a conflict of the mind and is not an evil. Physicians need to disclose all relationships to industry and/or other potential conflicts in order to maintain the trust of one's community, and in order to advance the best science.
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 | 0.581 | 0.695 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.020 | 0.009 |
| Insufficient payload (model declined to judge) | 0.021 | 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; 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".