P.065 The impact of a risk algorithm on time-to-care: targeting triage for acute cerebrovascular syndrome (ACVS) patients in a rapid TIA clinic
Bibliographic record
Abstract
Background: Approximately, one-third of TIA clinics use the ABCD2 score to triage referrals. However, the usefulness of the score is limited because of its low specificity for non-cerebrovascular/mimic conditions. Timely access of referred patients to specialized TIA clinics may reduce recurrent stroke. Methods: The SpecTRA project implemented a novel electronic triage system in the TIA clinic that services Vancouver Island (BC), which replaced the existing ABCD2 triage model. A clinical classifier generating an ACVS probability score was calculated on the basis of the clinic referral form information. Next, a time-varying ABCD2-based risk score derived from Johnston et al. (2007) was calculated, which is then weighted by the ACVS probability score to produce a finalized triage score. Time-to-care was compared pre- (2013/14) and post- (2014/15) implementation. Results: One year results show a statistically significant improvement in that time-to-care for ACVS patients (ABCD2 4/5) was one day earlier with the new triage system (median= 4days since symptom onset; N=250) compared to the previous year (median=5days; N=255) (Mann-Whitney U=38130, p< 0.001). No difference in unit arrival times (median= 5days) for non-cerebrovascular patients was observed (Mann-Whitney U=5563, p= 0.15). Conclusions: The performance of our ACVS triage system highlights quality improvement potential in time-to-care for outpatient TIA clinics.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".