Implementation of a patient selection protocol for intra-arterial therapy increases treatment rates in patients with acute ischemic stroke
Bibliographic record
Abstract
BACKGROUND: Strategies for patient selection for intra-arterial therapy (IAT) in acute ischemic stroke (AIS) are highly variable. The degree of protocol adoption and treatment rates associated with implementation of a service-wide patient selection IAT protocol were assessed. METHODS: All patients with AIS prospectively recorded in our stroke database from January 2007 to June 2009 were reviewed. The IAT patient selection protocol was implemented in March 2008. Patients were defined as likely to benefit (LTB) from IAT if they had brain imaging completed within 6 h from last known well time, NIH Stroke Scale score ≥ 8, infarct volume ≤ 100 ml and evidence of proximal artery occlusion. RESULTS: Of 1348 subjects identified, 118 (8.7%) met the criteria for LTB and 62 (52%) underwent IAT. There was a significant increase in rates of IAT among LTB patients after protocol implementation (61% vs 40%, p<0.02). In LTB patients, factors associated with IAT were stroke duration (OR 0.78, 95% CI 0.6 to 0.9 per hour), arrival within later calendar months during study period (OR 1.1, 95% CI 1.02 to 1.2 per month), intravenous tissue plasminogen activator (OR 0.6, 95% CI 0.4 to 0.9) and age (OR 0.98, 95% CI 0.95 to 1.02 per year). After multivariable adjustment, only stroke duration (OR 0.65, 95% CI 0.5 to 0.8 per hour) remained an independent predictor of IAT. CONCLUSIONS: Most patients with AIS did not meet our criteria for LTB and only 52% of those defined as LTB received IAT. Protocol adoption increased the use of IAT over time; however, further exploration of factors associated with the reasons for non-treatment and the impact of IAT on outcomes is necessary.
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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 | 0.018 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".