Predictive Value of the Ontario Prehospital Stroke Screening Tool for the Identification of Patients with Acute Stroke
Bibliographic record
Abstract
BACKGROUND: In 2005, a prehospital stroke screening tool was implemented in Toronto, Ontario, Canada. Patients identified by paramedics through the use of this tool in the field were transported to a regional stroke center under an acute stroke protocol. OBJECTIVE: To determine the positive predictive value (PPV) of the Ontario Prehospital Stroke Screening Tool for identification of acute stroke at a single stroke center. METHODS: We conducted a retrospective analysis of consecutive patients transported to a regional stroke center under the prehospital acute stroke protocol over a 12-month period. Final diagnoses, treatments, and outcomes were abstracted from a provincial registry. Rates of fibrinolysis were compared with those for the 12-month period prior to implementation of the stroke protocol. RESULTS: Three hundred twenty-five patients were triaged under the emergency medical services (EMS) acute stroke protocol over the study period. The PPV of the screening tool was 89.5% (95% confidence interval [CI]: 85.7-92.7%) for acute stroke. Thirty-four patients (11%) had nonstroke conditions, with the most common being seizure (4%). The rate of administration of tissue plasminogen activator (tPA) for all patients with suspected stroke increased from 5.9% to 10.1% (p = 0.04) compared with the rate in the 12-month period prior to implementation of the acute stroke protocol. The tPA rate for patients arriving under the stroke protocol was 17.2%. Most patients (75%) receiving tPA arrived from outside the hospital catchment area. CONCLUSIONS: In this preliminary study, the Ontario Prehospital Stroke Screening Tool had a high PPV for acute stroke and appeared to be effective for identifying patients who required triage to a single regional stroke center. Following implementation of a citywide acute stroke protocol using this screening tool, we observed an increase in the number of patients who were eligible for and received fibrinolysis at our stroke center.
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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 | 0.021 |
| 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 | 0.001 | 0.000 |
| 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".