Resetting The Dtn Clock : a Community Hospital Experience
Bibliographic record
Abstract
BackgroundLakeridge Health Oshawa is a District Stroke Centre that provides hyper-acute stroke care for the Durham Region. For three fiscal years the annual Door-To-Needle (DTN) times exceeded 65 minutes. The District Stroke Centre was forced to take a fresh look at the process.MethodAn environmental scan was conducted in an attempt to identify delays in treatment. This was followed by a literature review and poll of Stroke Centres across Ontario to examine what had been successfully implemented in other organizations. A working group was formed with representatives from all internal and external partners. Representatives shared drafts and solicited feedback from staff in their areas. Following several revisions a live simulation was conducted. A debrief followed and the final algorithm was completed on November 28, 2017. ED staff was engaged in the month preceding and following the go live date to review the new protocol and to provide feedback. The protocol went live on December 4, 2017. The CNS attended code strokes to offer support and education. ResultsThe recreated protocol included an overhead Stroke Alert prior to patient arrival, assessment of patient and transfer to CT on EMS stretcher, and delivery of tPA on the CT table. These changes have significantly decreased the DTN times. Three months following implementation DTN times have reduced by 30 minutes and continue to trend downward. The six-month data will be presented at Congress. ConclusionsSignificant improvements have occurred through thoughtful implementation strategies and staff commitment to improve patient outcome.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".