Potential therapeutic value of new drugs approved in Australia: a retrospective cohort study
Bibliographic record
Abstract
Objective To examine the potential therapeutic value of new medicines approved in the US and both approved and not approved in Australia. Methods A list of new medicines approved by the US Food and Drug Administration (FDA) between 1 January 2015 and 31 December 2020 was assembled and it was determined which of these medicines were also approved in Australia. Three metrics - first in class, priority review and therapeutic rating by two independent organisations - were used to determine the potential therapeutic value of the medicines. The percent of medicines with and without potential significant therapeutic value was compared using each of the three metrics. Results A total of 273 drugs were approved by the FDA, of which 147 (53.8%) were approved by the Therapeutic Goods Administration, the Australian regulator. For each of these three metrics, the percent of medicines with and without potential significant therapeutic value approved in Australia was the same: first in class (yes vs no: Chi-squared P = 0.8562), priority review (yes vs no: Chi-squared P = 0.4593), therapeutic rating (major/moderate vs little/no: Chi-squared P = 0.9006). Some of the 126 drugs not approved may be therapeutically important. Conclusions New medicines approved in the US between 2015 and 2020 without potential significant therapeutic value are as likely to be introduced into Australia as drugs with potential significant therapeutic value. Some potentially valuable drugs may not have been submitted for approval in Australia by the companies making them.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".