Abstract P123: Comparing Process, Performance Measures and Clinical Outcome Between Mobile Stroke Unit and Usual Care in Underserved Areas
Bibliographic record
Abstract
Background: The efficacy of mobile stroke units (MSU) in improving acute ischemic stroke (AIS) care in developing countries is unknown. We compared performance measures and treatment outcomes between MSU and usual care. Methods: We enrolled patients >18 years of age with a diagnosis of AIS who presented with onset to door < 4.5 hours and stroke code was alert either at the Siriraj MSU (SiMSU) or at the emergency department (ED). The same AIS treatment guideline for intravenous tPA or endovascular treatment (EVT) were applied in both groups. Demographic data, type(s) of reperfusion therapy (tPA and/ or EVT) were recorded. Stroke onset to door (OD), door to CT (DCT), door to needle (DN), and door to puncture (DP) time were collected. Stroke outcome was measured by the modified Rankin Score (mRS) at 3 months. Results: A total of 519 AIS patients (130 from SiMSU and 389 from ED) were enrolled between June 2018 and December 2019. The mean age ( + SD) was 66 ( + 13.9), 254 (48.9%) were male. AIS key process time measures were significantly shorter in the SiMSU with a mean + SD DCT time of 7 + 4.9 vs 16 + 13.7 minutes (p< 0.01), DN time of 21 + 7 vs 34 + 13.8 minutes (p< 0.01) and DP time of 71 + 20 vs 94 + 44.6 minutes, (p<0.01) in SiMSU and ED respectively. Although EVT rate was similar, intravenous tPA rate was significantly higher in SiMSU 59.2% vs 34.4% (p< 0.01). The mRS of 0-2 at 3 months was significantly higher in SiMSU than the ED (67.5% vs 57.9%, p=0.01). Conclusions: The SiMSU significantly shortened door to reperfusion treatment, increased access to IV t-PA and improved stroke outcomes by reducing disability at 3 months when compare to standard of care (i.e. ED arrival). This working paradigm could be implemented in other similar healthcare settings to improved AIS care.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".