Who is in the emergency room matters when we talk about door-to-needle time: a single-center experience
Bibliographic record
Abstract
Abstract Background The efficacy of intravenous thrombolysis (IVT) is time-dependent. Objective To compare the door-to-needle (DTN) time of stroke neurologists (SNs) versus non-stroke neurologists (NSNs) and emergency room physicians (EPs). Additionally, we aimed to determine elements associated with DTN ≤ 20 minutes. Methods Prospective study of patients with IVT treated at Clínica Alemana between June 2016 and September 2021. Results A total of 301 patients underwent treatment for IVT. The mean DTN time was 43.3 ± 23.6 minutes. One hundred seventy-three (57.4%) patients were evaluated by SNs, 122 (40.5%) by NSNs, and 6 (2.1%) by EPs. The mean DTN times were 40.8 ± 23, 46 ± 24.7, and 58 ± 22.5 minutes, respectively. Door-to-needle time ≤ 20 minutes occurred more frequently when patients were treated by SNs compared to NSNs and EPs: 15%, 4%, and 0%, respectively (odds ratio [OR]: 4.3, 95% confidence interval [95%CI]: 1.66–11.5, p = 0.004). In univariate analysis DTN time ≤ 20 minutes was associated with treatment by a SN (p = 0.002), coronavirus disease 2019 pandemic period (p = 0.21), time to emergency room (ER) (p = 0.21), presence of diabetes (p = 0.142), hypercholesterolemia (p = 0.007), atrial fibrillation (p < 0.09), score on the National Institutes of Health Stroke Scale (NIHSS) (p = 0.001), lower systolic (p = 0.143) and diastolic (p = 0.21) blood pressures, the Alberta Stroke Program Early CT Score (ASPECTS; p = 0.09), vessel occlusion (p = 0.05), use of tenecteplase (p = 0.18), thrombectomy (p = 0.13), and years of experience of the physician (p < 0.001). After multivariate analysis, being treated by a SN (OR: 3.95; 95%CI: 1.44–10.8; p = 0.007), NIHSS (OR: 1.07; 95%CI: 1.02–1.12; p < 0.002) and lower systolic blood pressure (OR: 0.98; 95%CI: 0.96–0.99; p < 0.003) remained significant. Conclusion Treatment by a SN resulted in a higher probability of treating the patient in a DTN time within 20 minutes.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".