Bibliographic record
Abstract
Interferons were introduced for multiple sclerosis in the early 1990s, after US-Canadian trials showed effects on clinical relapse rate and magnetic resonance imaging (MRI) spots, which were taken as surrogate outcomes for disability.1 The drug companies who marketed the interferons, and later glatiramer acetate, were given extended patent protection under the Orphan Drug Act. Under the terms of this act surrogate markers of response to treatment can be relied on if experts certify their validity. The lack of data on hard outcomes of disability, such as the need to use a stick or wheelchair, was accepted because multiple sclerosis is a 30-40 year disease, with only half of those affected becoming moderately disabled in a decade, and keeping trials intact beyond a few years proved difficult. Many specialists thought the visually obvious spots on MRI “were the disease.” As a result MRI scanning soon became indispensable for multiple sclerosis trials and individual high profile MRI centres capitalised on lucrative contracts with industry. Over the next two decades, little effort was made to validate the suppression of MRI spots against hard disease outcomes. Amid the enthusiasm for short term MRI monitoring of the impact of interferons, their lack of impact on long term disability (despite …
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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 | 0.071 | 0.344 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.012 | 0.004 |
| Research integrity | 0.064 | 0.068 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".