Bibliographic record
Abstract
Although fatigue is widely accepted to be one of the most common and disabling symptoms of multiple sclerosis (MS), it remains difficult to define and quantify, and its cause remains poorly understood.1 In general, management of fatigue begins with identification and amelioration of other factors contributing to it, such as depression, pain, sleep disorders, and comorbid medical conditions. Nonpharmacologic treatments, including graded exercise training,2 energy management strategies,3 and cooling therapy,4 have been tried, but evidence supporting their effectiveness is limited. Several pharmacologic treatments have been tried as well. Two studies reported in this issue of Neurology provide further information on the difficulties of treating this distressing MS symptom, one suggesting that a medication commonly prescribed for fatigue is ineffective,5 the other suggesting that an even more commonly used medication may have some benefit.6 The only treatments previously demonstrated to have an effect on fatigue in placebo-controlled clinical trials are …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".