Provision of stroke thrombolysis services in New Zealand: changes between 2011 and 2016.
Bibliographic record
Abstract
AIMS: To obtain an overall picture of the organisation of stroke thrombolysis provision in New Zealand hospitals and compare changes between 2011 and 2016. METHODS: Surveys were distributed to all New Zealand district health boards (DHBs) in 2011 and 2016, and included questions about the infrastructure, staffing, training, guidelines and audit provided for stroke thrombolysis. RESULTS: Responses were received from all DHBs, with 86% offering stroke thrombolysis in 2011 and 100% in 2016. In 2016, thrombolysis rosters of large DHBs (those with a population >250,000 people) had a mean (range) of 14 (5-34) clinicians, approximately double that of medium-sized DHBs (population 125-250,000) who had eight (3-15) and small DHBs (population <125,000) with seven, (2-13) clinicians. While a similar distribution of senior medical officer clinical specialty was seen across medium and small DHBs in both years, large DHBs in 2016 had a higher number of neurologists (5, 1-12) and an increasing number of general physicians (8, 0-30) rostered to provide thrombolysis compared to 2011. Thrombolysis services at medium and small DHBs are chiefly managed by general physicians and geriatricians, while telestroke support was only available in three medium-sized DHBs. In 2016, all hospitals had developed thrombolysis guidelines and audited thrombolysed patients in the National Stroke Thrombolysis Register, which is an improvement compared with 2011 when only seven (39%) DHBs reported regular audit. Challenges in staffing and training remain greatest in smaller and geographically isolated DHBs. CONCLUSION: While there have been improvements in the provision of stroke thrombolysis throughout New Zealand, regional variations in service quality remains. The needs for better solutions to geographical barriers and formal training must be addressed as priorities.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".