Bibliographic record
Abstract
Pens, post-its, staplers, notebooks, umbrellas, meals, rounds of golf, celebrity autographs, and so on-for many years, industry lavished physicians and other healthcare providers with giveaways.Recipients viewed them simply as gifts, courtesies or gratuities, but definitely not incentives to reciprocate.We all believed that we were immune to this influence and that it did not influence our primary responsibility as patient advocates.Yet, social scientists have clearly demonstrated that the impulse to reciprocate for even small gifts is a powerful influence on people's behaviour 1 -and there is no data to suggest that physicians are immune to normal human behavioural responses!We can do without the freebies.Indeed, we should.But without partnerships with industry, we would impoverish research initiatives that ultimately save lives.There is a balance to be struck, and disclosure rules that promote transparency point the way.As the years have passed, the number of drug and device companies has increased, and so has the competition between them.Since a corporation is primarily accountable to its shareholders, its representatives who need to sell their products will tend to fall back on proven ways of influencing physicians. 2 So it should be no surprise that, as the pressure on industry representatives to sell, sell, sell has intensified, so have measures to mitigate the impact of their efforts on patient care.Hence the new "sunshine laws" in the United States and, in Canada, University Policies on Conflict of Interest, Conflict of Commitment, CME guidelines, Medical Association Codes of Ethics, Standards of Industry in Medical Education and Rules regarding Relationships with Industry.These policies all intend to ensure that physicians' only objective-conscious or subconscious-is to advance the health of our patients, not the private interests of health professionals, industry or any other third party.This is not a good guy versus bad guy issue.Industry is not our enemy, but a great friend.There are huge benefits of working together, with appropriate (but not overly excessive) oversight.Industry-funded, contract-based, yet scientifically unbiased research in academic centres will result in the development of exciting new drugs and innovative technologies.We should not fear sunshine-it is, after all, the best disinfectant-but we can embrace transparency without compromising productive partnerships in the public interest; we should be sure not to throw the baby out with the (logo-embossed) bath towel.
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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.032 | 0.029 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".