Frequency and Prognostic Significance of Clinical Fluctuations Before Hospital Arrival in Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Clinical fluctuations in ischemic stroke symptoms are common, but fluctuations before hospital arrival have not been previously characterized. METHODS: A standardized qualitative assessment of fluctuations before hospital arrival was obtained in an observational study that enrolled patients with mild ischemic stroke symptoms (National Institutes of Health Stroke Scale [NIHSS] score of 0-5) present on arrival to hospital within 4.5 hours of onset, in a subset of 100 hospitals participating in the Get With The Guidelines-Stroke quality improvement program. The number of fluctuations, direction, and the overall improvement or worsening was recorded based on reports from the patient, family, or paramedics. Baseline NIHSS on arrival and at 72 hours (or discharge if before) and final diagnosis and stroke subtype were collected. Outcomes at 90 days included the modified Rankin Scale, Barthel Index, Stroke Impact Scale 16, and European Quality of Life. Prehospital fluctuations were examined in relation to hospital NIHSS change (admission to 72 hours or discharge) and 90-day outcomes. RESULTS: Among 1588 participants, prehospital fluctuations, consisting of improvement, worsening, or both were observed in 35.5%: 25.1% improved once, 5.3% worsened once, and 5.1% had more than 1 fluctuation. Those who improved were less likely and those who worsened were more likely to receive alteplase. Those who improved before hospital arrival had lower change in the hospital NIHSS than those who did not fluctuate. Better adjusted 90-day outcomes were noted in those with prehospital improvement compared to those without any fluctuations. CONCLUSIONS: Fluctuations in neurological symptoms and signs are common in the prehospital setting. Prehospital improvement was associated with better 90-day outcomes, controlling for admission NIHSS and alteplase treatment. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02072681.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".