Bibliographic record
Abstract
Three interferon-β (IFNβ) preparations are approved for the treatment of MS: two throughout the world (Betaferon; Schering AG, Berlin, Germany, and Avonex; Biogen, Cambridge, MA) and one outside the United States (Rebif; Ares-Serono, Geneva, Switzerland). Controversy persists regarding the relation of therapeutic efficacy of the different IFNβ-1a and IFNβ-1b products. Direct comparison of different clinical trials is problematic because of their variation in design and control groups, and differences in type, dose, and route of IFN administration. One approach to clarifying the relation of the biologic and therapeutic efficacy of IFN is to monitor induction of one or more of the scores of IFN-responsive genes, which hopefully will correlate with clinical efficacy. In this issue of Neurology , Deisenhammer et al.1 compared the capacity of the three clinically approved IFNβ preparations to induce MxA, an anti-myxovirus protein (anti-influenza), in vitro and in vivo. Compared to other genes, such as β2-microglobulin, …
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 | 0.006 | 0.020 |
| 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 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.021 | 0.037 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".