Stroke reperfusion treatment trends in New Zealand: 2019 & 2020.
Bibliographic record
Abstract
AIM: This study assessed stroke reperfusion treatments trends in 2019 and 2020 with comparison back to 2015. Additional analyses looked at differences by sex and ethnicity. METHOD: The National Stroke Register contains data on all stroke patients who received reperfusion therapies since 2015. Outcomes included treatment rates, delays, mortality and complications by year, sex, and ethnicity. Continuous variables were compared using the Wilcoxon Rank-Sum Test and presented as p-values. Rate-based results were compared using incidence rate comparison and presented as p-values +/- 95% confidence intervals. RESULTS: In 2020, 11.3% (828/7333) received intravenous thrombolysis (IVT) and 5.5% (404/7333) underwent stroke clot retrieval (SCR), increasing from 6.5% (389/5963) and 0.5% (30/5963) in 2015, respectively. Among reperfused patients (IVT, SCR, both), 8.3% had died at seven days and 3.0% (29/959) experienced sICH. Door-to-treatment time was stable between 2019 and 2020, with median (IQR) of 61 (44-84) and 61 (41-87) minutes, respectively. Initial presentation to a SCR centre was associated with shorter onset-to-reperfusion time of 286 (206-566) minutes, compared with 403 (295-550) minutes (p<0.001). While onset-to-door time was shorter for Māori (72 (44-112) minutes, p<0.001) and Pacific patients (70 (48-105) minutes, p=0.03) compared with NZ Europeans, door-to-needle time was longer in Māori (66 (48-88) compared to 59 (41-83) minutes (p=0.001). Female (73.7+/15.3 years) patients were on average 4.4 years older than males (69.3+/-14.6 years) and less likely to receive thrombolysis (12.7% vs 14.9%, p=0.02). CONCLUSION: Reperfusion therapy rates continue to rise, now driven by increasing rates of SCR. Longer door-to-needle time in Māori and lower reperfusion rates in women require further exploration and attention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".