Bibliographic record
Abstract
BACKGROUND: Numerous therapeutic interventions have been developed in the neurosciences. Clinicians need summary measures about efficacy of therapies that derive from the best available evidence, and that can be readily extrapolated to clinical practice. The number needed to treat (NNT) is intuitive and clinically applicable. We provide clinicians with a single source that summarizes important therapies in the main neurological and neurosurgical areas. METHODS: Critically appraised evidence about therapies in the neurosciences was obtained from meta-analyses in all neurosciences groups in the Cochrane library, and from critically appraised topics at the University of Western Ontario. Therapies were included if they were deemed relevant and if outcomes were dichotomous. For each therapy, we obtained absolute risk differences and their 95% confidence intervals (CI), the corresponding NNTs, control and experimental event rates, and the time-frame of the outcome assessment. RESULTS: We assembled a table of NNTs for 87 interventions in ten disease categories, deriving from meta-analyses (70%) or randomized controlled trials (30%), and assessing surgical interventions (7%), procedures (9%) or pharmacological treatments (84%). The NNTs varied widely, ranging from 1 in the use of epidural blood patch for post-dural puncture headache to 4608 for meningococcal vaccination. Preventative interventions had substantially larger NNTs. Time-frames were inappropriately short for many chronic conditions. CONCLUSIONS: Large collections of NNTs provide useful, updateable summaries of therapeutic effects in the neurosciences, an increasingly interventional clinical field.
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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 | 0.063 | 0.235 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".