US drug regulator rejects accusations that it unfairly targets Indian generic firms
Bibliographic record
Abstract
The US drugs regulator has defended its actions against Indian generic drug manufacturers who flout safety and quality standards. The regulator said that product safety was its top priority and that it would take any action deemed necessary to protect consumers. Margaret Hamburg, the US Food and Drug Administration’s commissioner, was in India for a 10 day visit from 10 February. She angered some Indian officials, who claimed that the FDA was unduly targeting Indian companies for substandard practices. India supplies about 40% of the generic and over-the-counter drugs consumed in the United States, making it the second largest supplier (after Canada). But Indian drug manufacturers accounted for half of the warning letters issued by the FDA in 2013. In January the FDA banned the import of drugs made at two further facilities run by the Indian generics manufacturer Ranbaxy Laboratories after finding problems with the manufacturing …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".