GLAD TIDINGS FOR INDIAN PHARMACEUTICAL INDUSTRY
Bibliographic record
Abstract
Dear Reader, It is indeed heartening to note that the Indian Government has notified Rules for fast-track launch of new, breakthrough drugs including block-buster molecules, waiving or bypassing clinical trials in India under Chapter V of the Rules, where the relevant drug has already been approved in specified countries, such as the European Union, US, UK, Japan, Australia and Canada. This decision was announced through an executive order specifying the list of developed countries under Rule 101 of the New Drugs and Clinical Trials Rules (NCDT) 2019, issued by the DCGI, Dr. Rajeev Singh Raghuvanshi. For the time being, the list of drugs covered under the order for approval of new drugs, under Chapter X are restricted to those for treatment of orphan drugs (rare diseases), new drugs for special defence purposes and those used in pandemic diseases, gene and cellular therapy products. It is further to be noted that drugs having significant therapeutic advances over the standard care, are also covered under this notification waiving domestic clinical trials. Indirectly, this waiver will not only help State Government and other public procurement agencies such as DGHS and Ayushman Bharat to reduce expenditure in their budgets, but also do away with animal trials in India in line with Indian and global initiatives. This initiative to selectively waive clinical trials will be a boon to research-based Indian Pharma companies too, for introducing generic equivalents of these drugs having significant therapeutic benefits to patients.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.024 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.019 | 0.027 |
| Insufficient payload (model declined to judge) | 0.159 | 0.146 |
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".