Trends in New Zealand stroke thrombolysis treatment rates.
Bibliographic record
Abstract
AIMS: To describe trends in treatment delays and short-term outcome over the first 18 months of the New Zealand stroke thrombolysis register. METHODS: The National Stroke Network introduced a central register of all ischaemic stroke patients treated with intravenous alteplase on January 1, 2015. The aim was to increase thrombolysis treatment rates and drive improvements in safety. RESULTS: From January 1, 2015 to June 30, 2016, alteplase was given to 623 patients [344 men, mean (range) age 70 (22-98) years] out of a total of 8,857 ischaemic and unspecified stroke patients, giving a thrombolysis rate of 7.0%. Between the first and second halves of the audit, there were more patients thrombolysed [350 of 4,456 (7.9%) versus 273 of 4,401 (6.8%); p=0.001] and more treated within 60 minutes of hospital arrival [137 of 325 (42%) versus 71 of 250 (28%), p=0.001]. Door-to-needle time reduced from 77 minutes to 64 minutes (p=0.002) and the onset-to-treatment reduced from 162 minutes to 140 minutes (p=0.070). Rates of symptomatic intracranial haemorrhage (4.3% patients) and survival at day seven (93%) were stable. CONCLUSIONS: There have been improvements in stroke thrombolysis rates and treatment delays in New Zealand hospitals since the institution of the National Stroke Network thrombolysis register. The Network will continue to adjust key performance indicators, and stroke thrombolysis targets for individual DHBs have been increased to 8% for 2017 and 10% for 2018.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".