Bibliographic record
Abstract
Objective: To see if a ‘practical’ subset of the NIHSS could be a reasonable measurement of post acute-stroke progress. Background: Acute stroke patients require monitoring following hospital admission to detect complications or deterioration. Traditionally a Glascow Coma scale or similar is used (i.e. Neurovital signs q4h). However, the GCS is too insensitive to uncover some stroke deterioration. The NIHSS is robust and widely accepted, but requires extensive training and is time intensive to perform. We investigate whether a subset of the NIHSS can detect most worsening, but is easier to train and quicker to perform. Methods: 38 prospective patients had a GCS & full NIHSS acutely and at 24-48 hours. Results: GCS was too insensitive, and the NIH K-Mod was easy to train and perform, and excepting occipital strokes was equally effective at showing improvement and raising alarm for deterioration. Conclusions: The NIH K-Mod SS is a clinically effective tool for monitoring post acute stroke patients, is more sensitive than the GCS and less resource requiring than the full NIHSS.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".