Bibliographic record
Abstract
Physicians need to stay abreast of information about emerging drugs and devices, but the time pressures of clinical practice may limit their ability to do so independently. The companies that manufacture and sell these products have the resources and the motivation to “educate” physicians but cannot be expected to distinguish their marketing goals from physicians’ educational needs. Physicians’ professional associations and regulatory bodies, as well as medical journal publishers and editors, drug and device regulatory agencies, and academic medical institutions, have long debated their respective roles and responsibilities in ensuring the safety, efficacy, and probity of prescribing in light of these pressures and interests.\nOne current context of this long-standing struggle is the “speakers’ bureau” system, in which pharmaceutical, biotechnology, and medical device companies recruit and train physicians to deliver information about products to other physicians, in exchange for a fee or other considerations, such as professional development opportunities. Participants in the system argue that physicians are best situated to deliver accurate information about new drugs and devices to other physicians and that industry is best placed to fund such communication. Critics reply that the speakers’ bureau system raises significant concerns about ethics and professionalism and that it is part of a complex system of drug promotion and relationship-building with physicians that contributes to irrational prescribing, inflated health care costs, and even harm to patients or society more generally. Some steps have been taken toward limiting participation in speakers’ bureaus. The American Association of Medical Colleges (AAMC), in a report endorsed by the Association of Faculties of Medicine of Canada (AFMC), has stated that faculty participation in speakers’ bureaus should be strongly discouraged and that faculty, residents, and students should be prohibited from attending such events. Furthermore, in the United States, lawsuit settlements and health care reform (i.e., the Physician Payment Sunshine Act, passed as a part of the Patient Protection and Affordable Care Act) are bringing some transparency to speakers’ bureau arrangements.
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 | 0.030 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.076 | 0.032 |
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".