O-006 Influence of geolocational factors on patient outcomes post EVT: a population based analysis of transport over long distances in the province of Saskatchewan
Bibliographic record
Abstract
Background and Aim Endovascular treatment of acute stroke necessitates fast transport across large geographic distances which influences the time to treatment and ultimately patient outcomes. While probabilistic models have been developed to postulate the influence of distance and processing times in drip and ship models,1 real life data on actual transport times and its influence on outcomes has been under studied in real-world populations. We retrospectively examined and analysed the transport registry of EVT treated patients across the large province of Saskatchewan. Methods A linked registry based on medical records and Optimize database was used to capture the journey from home to hospital in patients who underwent EVT from Aug 2017 to Dec 2022 across Saskatchewan. Transport modality of the patients and whether they were directly presented to CSC, or first presented to a peripheral hospital PSC was determined and the patient’s geolocation was mapped using the google distance matrix API. Communities were classified as large urban, medium, and small population centers based on stats Canada classification. Association of functionally independent outcomes mRS 0-2 was examined for transport type, community size, and Kilometer distance from home to CSC, and some via PSC. Influence of each Km of distance during each transport was examined on the shift of outcome using ordinal logistic regression. Results 303 patient that underwent thrombectomy over a 5 year period were examined of which 55% presented directly via EMS, 36% Transferred from peripheral sites, and 9% were in-patient treated. Compared to the direct presentation, the transfer patients did not have a significantly worse outcomes OR 0.8 p=0.382 (CI 0.16 - 0.7) as the direct patient, while the inpatients did worse overall OR 0.38 p=0.042 (CI 0.16- 0.97). Size of community the patient presented from or transferred from did not influence outcomes. In an adjusted analysis number of Km of transport did not have an influence on outcomes OR 1.00 p=0.827 (CI .99- 1.00). Conclusions Real world geographic factors influence time to treatment, however patient specific factors play a great role in determining patient’s overall outcome during long transports. Factors such as collaterals, occlusion location, and initial aspects are more important than number of kilometers distance travelled or transfer from PSC alone. In an efficient transport system, centralized treatment at the CSC is a viable solution and does not preclude achieving functionally independent outcomes. Disclosures N. Kashani: None. A. Zhou: None. J. Ospel: None. N. Singh: None. A. Gardner: None. A. Persad: None. R. Otani: None. U. Ahmed: None. L. Peeling: None. M. Kelly: None.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".