Peggy McNult, Manager, American Society for
Bibliographic record
Abstract
Dr. Sutton wrote in his March editorial about recent process breakdowns in quality control and their im-pact on the pharmaceutical industry. As a result, the FDA is conducting more rigorous inspections, while recent economic travails have resulted in a reduction of experienced staff and fewer training dollars. Around-the-clock news coverage has led to a well-informed public who are demanding more assurances of quality in our global market. From contaminated heparin from China, hydrocortisone cream contaminated with yeast manufactured in Canada, and fungal contaminated tablets from China, the public is looking to the FDA to keep im-ported and U.S. products safe. The global economy and frequency of quality mis-haps are forcing us to think how we can assure quality while keeping costs down. Most laborato-ries follow the standards of the United States Phar-macopeia (USP), the official public standards– setting authority for all prescription and over–the– counter medicines and other health care products manufactured or sold in the U.S. USP standards are followed by more than 130 countries to ensure pub-lic health. USP <1117> Microbiological Best Laboratory
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.067 | 0.039 |
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".