Evaluating the Diagnostic Performance of Prehospital Stroke Scales Across the Range of Deficit Severity: Analysis of the Prehospital Triage of Patients With Suspected Stroke Study
Bibliographic record
Abstract
Background: The usefulness of prehospital scales for identifying anterior circulation large vessel occlusion (aLVO) in patients with suspected stroke may vary depending on the severity of their presentation. The performance of these scales across the spectrum of deficit severity is unclear. The aim of this study was to evaluate the diagnostic performance of 8 prehospital scales for identifying aLVO across the spectrum of deficit severity. Methods: We used data from the PRESTO study (Prehospital Triage of Patients With Suspected Stroke Symptoms), a prospective observational study comparing prehospital stroke scales in detecting aLVO in suspected stroke patients. We used the National Institutes of Health Stroke Scale (NIHSS) score, assessed in-hospital, as a proxy for the Clinical Global Impression of stroke severity during prehospital assessment by paramedics. We calculated the sensitivity, specificity, positive predictive value, negative predictive value, and the difference in aLVO probabilities with a positive or negative prehospital scale test (ΔP aLVO ) for each scale for mild (NIHSS 0–4), intermediate (NIHSS 5–9), moderate (NIHSS 10–14), and severe deficits (NIHSS≥15). Results: Among 1033 patients with suspected stroke, 119 (11.5%) had an aLVO, of whom 19 (16.0%) had mild, 25 (21.0%) had intermediate, 30 (25.2%) had moderate, and 45 (37.8%) had severe deficits. The scales had low sensitivity and positive predictive value in patients with mild-intermediate deficits, and poor specificity, negative predictive value, and accuracy with moderate-severe deficits. Positive results achieved the highest ΔP aLVO in patients with mild deficits. Negative results achieved the highest ΔP aLVO with severe deficits, but the probability of aLVO with a negative result in the severe range was higher than with a positive test in the mild range. Conclusions: Commonly-used prehospital stroke scales show variable performance across the range of deficit severity. Probability of aLVO remains high with a negative test in severely affected patients. Studies reporting prehospital stroke scale performance should be appraised in the context of the NIHSS distribution of their samples.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".