Conflicts of interest and expertise of independent commenters in news stories about medical research
Bibliographic record
Abstract
BACKGROUND: Media coverage of medical research influences the views and behaviours of clinicians, scientists and members of the public. We examined how frequently commenters in news stories about medical research have relevant expertise and have academic and financial conflicts, how often such conflicts are reported and whether there are associations between the conflicts and the disposition of the comments toward the findings of the source research. METHODS: We analyzed 104 independent comments in news stories on original clinical research published in high-impact medical journals from Jan. 1 to Mar. 31, 2013, and 21 related journal editorials. Main outcomes were prevalence of relevant academic and clinical expertise, prevalence and reporting of academic and financial conflicts of interest, and disposition of comments toward study findings. RESULTS: = 0.007). Among the 104 comments, an academic conflict of interest was present for 56 (54%), of which 25 (45%) were reported in the news stories. A financial conflict of interest was present for 33 (32%) of the comments, of which 11 (33%) were reported. When commenters' conflicts of interest were congruent with the findings of the source research, 97% and 93% of comments associated with academic and financial conflicts of interest, respectively, were favourably disposed toward the research. These values were 16% and 17%, respectively, when the conflicts of interest were not congruent with the research findings. INTERPRETATION: Independent commenters in new stories about medical research may lack relevant academic or clinical expertise. Academic or financial conflicts of interest were frequently present among independent commenters but infrequently reported, and were often associated with the disposition of comments about the source research.
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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 | 0.037 | 0.368 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".