Conflict of interest in peer-reviewed medical journals: The world association of medical editors position on a challenging problem
Bibliographic record
Abstract
Conflict of interest in medical publishing exists when a participant's private interests compete with his or her responsibilities to the scientific community, readers and society. While conflict of interest is common, it reaches the level of concern when 'a reasonable observer might wonder if the individual's behavior or judgment was motivated by his or her competing interests'.[1] Having a competing interest does not, in itself, imply wrongdoing. However, it can undermine the credibility of research results and damage public trust in medical journals. In recent years, the extent of conflict of interest in medical journal articles has been increasingly recognized. Medical journals and the popular media have published numerous examples of competing interests that seemed to have biased published reports.[2–4] Organizations have expressed concern for the effects of conflicts of interest on research,[5] publication[1,6,7] teaching[8] and continuing medical and nursing education.[9] The World Association of Medical Editors (WAME) is one of the institutions engaged in this discussion. WAME was established in 1995[10,11] to facilitate worldwide cooperation and communication among editors of peer-reviewed journals, improve editorial standards and promote professionalism in medical editing.[12] Membership in WAME is open to all editors of peer-reviewed biomedical journals worldwide; small journals in resource-poor countries are well represented. As of December 2009, WAME had 1595 individual members representing 965 journals in 92 countries. WAME has broad participation as there are no dues and WAME activities are largely carried out through the member list serve and the member password protected website. In March 2009, WAME released an updated policy statement, ‘Conflict of Interest in Peer-Reviewed Medical Journals’.[1] It details the issues WAME believes journals should address when establishing their own policies for conflict of interest. The editors of this journal thought that the issues were important enough to share with its readers. A summary of the statement is presented in Table 1 and the full statement[1] can be found on WAME's website.[12] Table 1 Summary of Key Elements for Peer-Reviewed Medical Journal's Conflict of Interest Policies
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 | 0.095 | 0.302 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.028 | 0.022 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.035 | 0.031 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".