[A Survey to Compare the Specifications and Stability of Anticancer Drugs in Japan, the United States, Canada, and Australia].
Bibliographic record
Abstract
Recently, expensive anticancer drugs such as molecularly targeted drugs have been reportedly ineffective. The use of drug vial optimization(DVO)has been proposed to overcome this problem. The specifications and stability of anticancer drugs in Japan were compared to those in other countries that used DVO, based on the results of the survey reported at the 2016 International Society of Oncology Pharmacy Practitioners meeting that compared the international specifications and stability of anticancer drugs. Our survey investigated expensive and frequently used anticancer drugs: 14 anticancer monoclonal anti- bodies(MABs)and 26 cytotoxic agents. About 29%(4/14)of the MABs and 54%(14/26)of the cytotoxic agents mar- keted in other countries were sold in larger vials than those marketed in Japan. About 67%(2/3)of theMABs and 38%(8/ 21)of the cytotoxic agents marketed in other countries had stability data of reconstitution obtained across longer test periods than those in Japan. About 29%(4/14)of theMABs and 50%(13/26)of the cytotoxic agents marketed in other countries had stability data of final dilution obtained across longer test periods than those in Japan. The stability data obtained in Japan were comparable to those obtained in 3 other countries that used DVO. However, some differences were noted in the specifications of anticancer drugs between Japan and other countries.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".