Prescription Drug Sampling — is the Status quo about to Change?
Bibliographic record
Abstract
Drug sampling plays a key role in the pharmaceutical marketing industry's attempts to reach the “prescribing audience” of health professionals. The traditional mechanism of pharmaceutical sales representatives giving drug samples to the physician as part of a “detailing” appointment is being supplemented by new mechanisms. In light of growing public and private concerns regarding the administration, management, and application of drug samples, the efforts to develop alternative approaches and the willingness of health professionals and pharmaceutical companies to consider these approaches are increasing. Success will depend on the acceptance and adoption of the approach by pharmaceutical manufacturers, physicians, pharmacists, policy-makers, insurers (public and private), regulators, as well as consumers. Given the number of stakeholders involved, it is not surprising that many of the attempts to introduce alternative methods have failed to achieve the critical level of participation necessary to advance these initiatives into an economically sustainable, broadly accepted model.
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 | 0.066 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".