Delayed Thrombectomy Center Arrival is Associated with Decreased Treatment Probability
Bibliographic record
Abstract
BACKGROUND: Endovascular thrombectomy (EVT) is effective in reducing disability in selected patients with stroke and large vessel occlusion (LVO), but access to this treatment is suboptimal. AIM: We examined the proportion of patients with LVO who did not receive EVT, the reasons for non-treatment, and the association between time from onset and probability of treatment. METHODS: We conducted a retrospective cohort study of consecutive patients with acute stroke and LVO presenting between January 2017 and June 2018. We used multivariable log-binomial models to determine the association between time and probability of treatment with and without adjustment for age, sex, dementia, active cancer, baseline disability, stroke severity, and evidence of ischemia on computerized tomography. RESULTS: We identified 256 patients (51% female, median age 74 [interquartile range, IQR 63.5, 82.5]), of whom 59% did not receive EVT. The main reasons for not treating with EVT were related to occlusion characteristics or infarct size. The median time from onset to EVT center arrival was longer among non-treated patients (218 minutes [142, 302]) than those who were treated (180 minutes [104, 265], p = 0.03). Among patients presenting within 6 hours of onset, the relative risk (RR) of receiving EVT decreased by 3% with every 10-minute delay in arrival to EVT center (adjusted RR 0.97 CI95 [0.95, 0.99]). This association was not found in the overall cohort. CONCLUSIONS: The proportion of patients with acute stroke and confirmed LVO who do not undergo EVT is substantial. Minimizing delays in arrival to EVT center may optimize the delivery of this treatment.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".