Doctors getting biggest payments from drug companies don’t declare them on new website
Bibliographic record
Abstract
Health professionals who are paid the most by UK drug companies for providing time and advice are the least likely to have voluntarily declared the payments, analysis of a new disclosure website indicates. The data show that 70% of healthcare professionals in receipt of payments from companies required to register details on a website hosted by the Association of the British Pharmaceutical Industry (ABPI) agreed to have the data disclosed. But the 30% who didn’t agree to disclosure received 52% of the payments registered (fig 1⇓). However, The BMJ has been unable so far to determine how many healthcare professionals are included in the website and how many are doctors. Fig 1 Healthcare professionals agreeing to disclosure of data Will Stahl-Timmins Mike Thomson, chief executive of the association, said that this meant that those receiving the largest payments were less likely to have agreed to have them disclosed. “I’m disappointed by that,” he said. “These are probably leading consultants whose services are highly valued by the companies. It’s understandable that we don’t want our neighbours to know how much we earn, but I hope that when they see this database published they may think …
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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 | 0.005 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.136 | 0.039 |
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".