Time to Hospital Admission and Start of Treatment in Patients with Ischemic Stroke in Northern Italy and Predictors of Delay
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Early treatment (i.e. thrombolysis) is crucial for a successful care of ischemic stroke. In the management of stroke, two phases are crucial: the pre-hospital and the in-hospital interval. This work investigated factors influencing pre- and in-hospital delay in a large geographic area of Northern Italy. METHODS: Enrolled were patients presenting with ischemic stroke in four administrative districts of Northern Italy (Como, Lecco, Sondrio and Varese) over a 4-month period. Pre-hospital time and in-hospital time with single management steps were recorded prospectively. Age, gender, recruiting hospital, EMS transport and triage codes, clinical severity and thrombolytic treatment were also recorded. Univariate and multivariate analysis of factors predicting pre- and in-hospital delay were performed. RESULTS: Median pre-hospital time and in-hospital time were, respectively, 120 min (interquartile range, IQR 62-271) and 150 min (IQR 80-214). Pre-hospital time was halved in patients hospitalized via EMS (p<0.001) and clinically more severe (p<0.001). At multivariate analysis, transport code was associated with delay at any time (p<0.05). CONCLUSIONS: EMS use and transport code predicted treatment delay in patients with ischemic stroke. A more intensive use of EMS and high urgency codes could help increase the number of stroke patients treated appropriately.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".