Progress in Clinical Neurosciences: Measuring the Benefit of Therapies for Neurological Disorders
Bibliographic record
Abstract
BACKGROUND: Clinicians in the neurosciences need to interpret and apply a growing body of evidence about therapy. METHODS: Using a clinical scenario about painful diabetic neuropathy and evidence about one treatment option, we review the advantages, limitations, and the clinical interpretation of commonly reported measures of effectiveness, emphasizing their application to the care of individual patients. RESULTS: Absolute differences between treatment and control groups (e.g., absolute risk difference) are clinically intuitive and preferable to relative measures (e.g., relative risk). The number needed to treat is particularly useful and clinically applicable. Continuous measures are best interpreted using thresholds of clinically important change, which provide information about the number of patients experiencing meaningful improvement or worsening. CONCLUSIONS: Using simple principles of evidence based care, clinicians can correctly interpret the common measures of treatment effectiveness and apply them to the care of individual patients.
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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 | 0.146 | 0.274 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".