Bibliographic record
Abstract
mAbs’ September/October 2009 issue highlights the promise and challenges of antibody therapeutics development. Representing promise, our mini-review series on novel antibodies currently undergoing regulatory review or recently approved continues in this issue. Previously published articles include mini-reviews of denosumab and ustekinumab (May/June 2009 issue) and ofatumumab (July/August 2009 issue). The September/October issue features articles on golimumab, tocilizumab and motavizumab. The mini-reviews present overviews of the completed and on-going clinical studies of these molecules. Anti-TNFα golimumab was approved in April 2009 by both the US Food and Drug Administration (FDA) and Health Canada as a treatment for rheumatoid arthritis (RA), psoriatic arthritis, and ankylosing spondylitis; anti-IL6R tocilizumab is approved in Japan and the European Union (EU), and is currently undergoing FDA review as a treatment for RA. The juxtaposition of these two mini-reviews provides an opportunity to easily compare summaries of the available clinical results. Future issues of mAbs will include mini-reviews of catumaxomab, canakinumab and raxibacumab, as well as any additional antibodies that enter regulatory review in 2009 and beyond.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.020 | 0.016 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".