Bibliographic record
Abstract
GP commissioning groups have accepted hundreds of payments from drug companies that they have not disclosed to patients and the public. Tom Moberly reports A BMJ investigation has uncovered the extent of payments from pharmaceutical companies to GP commissioning groups and the degree to which the deals are made public. Ever since clinical commissioning groups were launched in England in 2013, there have been concerns about the conflicts of interest among those who commission health services while also providing them.12 Now, to gain a full picture of the payments that private companies and charities are giving to CCGs, The BMJ has worked with Piotr Ozieranski, a lecturer in the department of social and policy sciences at the University of Bath, Shai Mulinari, a sociology researcher at Lund University in Sweden, and Emily Rickard, a research assistant in Bath’s department of social and policy sciences, who intend to publish the full findings of their research in the coming months. The BMJ received responses from all 207 CCGs in England after it made requests under freedom of information legislation about payments from private companies and charities. The data were compared with the details published by CCGs in their online public registries of declarations, which include payments from various sources (box 1). The responses to The BMJ ’s request showed that only two thirds of the 4600 payments—and just over a quarter of the value—that CCGs accepted from private companies and charities in 2015-16 and 2016-17 were listed in registers or declarations published by the CCGs. Box 1 ### Beyoncé tickets, sports matches, and VIP packages Details of the payments publicly declared by CCGs show the various ways in which other organisations interact with them. CCGs receive payments from charities and other private sector companies, such as consultancy firms and property investors, as well as from drug companies. For instance, in … RETURN TO TEXT
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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 | 0.012 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.097 | 0.043 |
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".