Reducing Door-to-Needle Times for Ischaemic Stroke to a Median of 30 Minutes at a Community Hospital
Bibliographic record
Abstract
BACKGROUND: Alteplase is an effective treatment for ischaemic stroke patients, and it is widely available at all primary stroke centres. The effectiveness of alteplase is highly time-dependent. Large tertiary centres have reported significant improvements in their door-to-needle (DTN) times. However, these same improvements have not been reported at community hospitals. METHODS: Red Deer Regional Hospital Centre (RDRHC) is a community hospital of 370 beds that serves approximately 150,000 people in their acute stroke catchment area. The RDRHC participated in a provincial DTN improvement initiative, and implemented a streamlined algorithm for the treatment of stroke patients. During this intervention period, they implemented the following changes: early alert of an incoming acute stroke patient to the neurologist and care team, meeting the patient immediately upon arrival, parallel work processes, keeping the patient on the Emergency Medical Service stretcher to the CT scanner, and administering alteplase in the imaging area. Door-to-needle data were collected from July 2007 to December 2017. RESULTS: A total of 289 patients were treated from July 2007 to December 2017. In the pre-intervention period, 165 patients received alteplase and the median DTN time was 77 minutes [interquartile range (IQR): 60-103 minutes]; in the post-intervention period, 104 patients received alteplase and the median DTN time was 30 minutes (IQR: 22-42 minutes) (p < 0.001). The annual number of patients that received alteplase increased from 9 to 29 in the pre-intervention period to annual numbers of 41 to 63 patients in the post-intervention period. CONCLUSION: Community hospitals staffed with community neurologists can achieve median DTN times of 30 minutes or less.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".