Bibliographic record
Abstract
After decades of debate over how best to provide patients with information about their medications at the point of dispensing, FDA wants to place the primary responsibility for the process in the hands of drug manufacturers. “We suggest that information about a manufactured product is best produced and tested by that manufacturer,” said Janet Woodcock, chief of FDA’s drugs division, during testimony on December 11 before the Senate Special Committee on Aging. Woodcock was referring to patient medication information (PMI). Examples of PMI include leaflets that are packaged along with mail-order prescription products or attached to the bag in which a patient’s medications are placed for pickup at a pharmacy. Manufacturer-produced PMI is used in the European Union, Canada, Japan, Australia, and New Zealand, Woodcock said. If implemented, FDA’s plan would replace the current U.S. system through which PMI is created in electronic form by ASHP and other providers of drug information and used by community pharmacies to generate leaflets for patients. FDA does not review or approve the leaflets, but their content and format are subject to FDA standards.
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 | 0.024 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.025 | 0.012 |
| Insufficient payload (model declined to judge) | 0.069 | 0.070 |
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".