Bibliographic record
Abstract
As the therapeutic revolution in neurology advances, the number of pharmacologic and regenerative therapies grows ever larger. With this abundance, however, comes the challenge of properly identifying which therapies warrant the substantial investments required for full-scale human trials; sensitive assessment of treatment effects is therefore paramount. Disease progression, as the sum of injury due to the disease minus the countervailing effects of the patient's own compensatory systems, is a relevant but hard to measure outcome. While many biomarkers track primary disease activity, most proposed regenerative therapies (e.g., stem cells, growth factors, genetic re-engineering) act primarily through compensation and repair. Unfortunately, comparatively few assays provide a sound measure of compensation or repair, and even fewer assays provide simultaneous information about both disease activity and compensation. A motor unit number estimate (MUNE) can be calculated by taking a value representing all the motor units subserved by a nerve and dividing by a similar value representing the typical single motor unit in that nerve (usually, such values are size parameters). Just as the familiar maximal compound motor action potential (CMAP), obtained during the routine motor nerve conduction study, provides an electrophysiologic measure of all the …
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 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".