Tissue Plasminogen Activator for Rural Referrals; the Effect of Stroke Team Notification Prior to Arrival
Bibliographic record
Abstract
P196 Background: Many rural community hospitals (RCH) in Southwestern Ontario lack a CT scanner. The stroke team (ST) in London provides tertiary care to these RCH. Advance notification of transfer of non-London patients (NLP) from RCH allows the ST to manage them from arrival at the LER. In contrast, the ST is notified of London patients (LP) after their arrival and registration in the London ER (LER). Objective: Assess feasibility of tPA administration to rural patients transferred to a tertiary care center. Methods: Mean symptom to LER, door to imaging, imaging to tPA, and door to tPA (DtPA) for LP and NLP times were compared. In-patients were excluded from the analysis. Results: Between Dec 1, 98 and Jun 30, 00, 61 patients were treated with tPA in London: 16 (26%) were NLP, 45 (74%) were local (37 LP, 8 in-patients). For NLP the mean symptom to RCH time was 37 mins, the mean RCH to LER distance was 41 miles (range 11–80) and the mean transfer time was 90 mins. (range 46–138). Symptom onset to LER time was significantly longer for NLP, but door to imaging, imaging to tPA, and DtPA were significantly lower (p Conclusions: 1. The establishment of a network of RCH and a tertiary center can extend the benefits of tPA to a rural population. 2. DtPA can be shortened if the ST manages the patients from arrival to the ER. This strategy could be applied to local patients if EMS notifies the ST of potential candidates for tPA.
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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 | 0.005 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".