Tia Management in The Ed (Time) : One Stroke Center'S Efforts To Improve Access To Timely Secondary Stroke Prevention Services
Bibliographic record
Abstract
A transient ischemic attack (TIA) is a brief episode of neurological dysfunction typically lasting under 60 minutes. Patients presenting with TIA are at increased risk of recurrent stroke, particularly within the first week following initial event. Health Sciences North (HSN) is a Regional Stroke Centre, located in Sudbury, Canada. In 2016, 161 people presented to ED with presentation of TIA. Managed solely by the ED, the Stroke Program is not involved in the visit. Recent data shows 28% of ED TIA patients are discharged home without a referral to the secondary stroke prevention clinic. When necessary, TIA patients are admitted. HSNu2019s annual average length of stay (LOS) at HSN in 2016-17 was 4.5 days. The stroke prevention clinic has limited service availability, receiving 375 referrals annually. The average wait time for the clinic, for those at highest risk is 2.7 days. Only 10% are seen in the timely manner set out by Canadian Stroke Best Practice Recommendations. The eight month pilot study TIME began January 1st 2018 whereby the Stroke Programu2019s Medical Lead physician will respond to all TIA presentations to ED to establish the TIA diagnosis, identify stroke mimics, facilitate best practice work-up, admit when very high risk features warrant, initiate secondary stroke prevention medications, dispense education package and refer to prevention clinic. Our hypothesis is that TIME will have a positive impact on % referrals to clinic at ED discharge; TIA admission rate; TIA inpatient LOS; wait time to clinic. Pilot evaluation will yield early results in October 2018.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".