P.037 Keeping track of time: emphasizing symptom onset-to-hospital time in stroke care
Bibliographic record
Abstract
Background: The Canadian Stroke Best Practice Recommendations target a median door-to-needle time of 30 minutes. However, brain tissue becomes damaged with any delay from symptom onset. Efficiencies may be gained prior to hospital arrival, by evaluating the timeliness of patient access to hospital from symptom onset, as well as by improving healthcare provider communication, prior to arrival of the patient. Methods: We engaged with hospital administration, paramedic services, allied health colleagues, physicians, and engineers, to develop Kairos, a secure online platform that healthcare providers can utilize to track progress en route to hospital, as well as to share pertinent stroke patient information, prior to arrival. Results: Kairos is built on React Native, allowing users to access it on android or iOS devices. Paramedics select patient identification, symptom onset time, and associated symptoms. The receiving hospital can add doctors to the patient’s thread, and the stroke team can prepare for patient arrival. Conclusions: We plan to measure the median symptom onset-to-hospital time in patients with strokes, and monitor the change in door-to-needle time following implementation at an Ontario Regional Stroke Centre.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".