Bibliographic record
Abstract
The history of biosimilars started at European Union (EU) in 2006 with one product; however, currently it has been recognized everywhere in the world and EU have highest 64 biosimilar products. United States Food & Drug Administration (USFDA) was little unadventurous with biosimilars; nevertheless, they approved the first biosimilar 09 years after EU approval and presently they have 28 biosimilars which are playing significant role in price cutting of branded biologics. They also have so many biosimilars in product pipeline. Economically emerging countries especially China & India are very aggressive with biosimilars. In view of easy regulation, cheap labor & other cost related factors they are in little advantageous than the rivalries. Under Pharmaceutical Benefits Scheme Australian government is encouraging biosimilars and they already approved 20 biosimilars. Japan, Korea, Canada, South Africa are also promoting biosimilars. However, it is worth mentioning that in spite of enormous potentiality and rapid growth till to date biosimilar market is insignificant compared to total pharmaceutical market and success of biosimilars will depend on the acceptance by the physicians, treatment cost reduction, trust on manufacturer, proper information, drug substitution, efficacy, safety etc.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".