Predictors of diagnostic neuroimaging delays among adults presenting with symptoms suggestive of acute stroke in Ontario: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Many studies have examined the timeliness of thrombolysis for acute ischemic stroke, but less is known about door-to-imaging time. We conducted a prospective cohort study to assess the timing of neuroimaging among patients with suspected acute stroke in the province of Ontario, Canada, and to examine factors associated with delays in neuroimaging. METHODS: We included all patients 18 years and older with suspected acute stroke seen at hospitals with neuroimaging capacity within the Ontario Stroke Registry between Apr. 1, 2010, and Mar. 31, 2011. We used a hierarchical, multivariable Cox proportional hazards model to evaluate the association between patient and hospital factors and the likelihood of receiving timely neuroimaging (≤ 25 min) after arrival in the emergency department. RESULTS: A total of 13 250 patients presented to an emergency department with stroke-like symptoms during the study period. Of the 3984 who arrived within 4 hours after symptom onset, 1087 (27.3%) had timely neuroimaging. The factors independently associated with an increased likelihood of timely neuroimaging were less time from symptom onset to presentation, more severe stroke, male sex, no history of stroke or transient ischemic attack, arrival to hospital from a setting other than home and presentation to a designated stroke centre or an urban hospital. INTERPRETATION: A minority of patients with stroke-like symptoms who presented within the 4-hour thrombolytic treatment window received timely neuroimaging. Neuroimaging delays were influenced by various patient and hospital factors, some of which are modifiable.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".