Bibliographic record
Abstract
The generic drug product market is projected to grow from US $15 billion in 2004 to US $27 billion in 2009 in the United States, and from US $9 billion to US $14 billion in Western Europe (1). Moreover, the growth opportunities for generic drug products in the near future are significant with an estimated US $100 billion worth of branded pharmaceutical products to go off patent by 2010 (1). The substantial growth of the world generics drug market has been driven by a number of factors, but in particular the need to contain public health care spending, including the expenditure on drug products. In response to the important growth of the generic pharmaceutical industry during the last 10 to 15 years, regulatory agencies in countries all over the world, such as the Food and Drug Administration (FDA) in the United States, Canada's Health Products and Food Branch (HPFB), and the European Medicines Agency (EMEA) in the European Union (EU), have established requirements which must be met by a generic drug product to receive marketing authorization (2,3)\n\n\nRead More: http://informahealthcare.com/doi/abs/10.3109/9781420020021.005
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.259 | 0.228 |
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".