Improvement in Thrombolytic Therapy Administration in Acute Stroke with Feedback
Bibliographic record
Abstract
BACKGROUND: The benefits of intravenous recombinant tissue plasminogen activator (rt-PA) in acute ischemic stroke is time dependent. Guidelines recommend a door-to-needle (DTN) time of less than 60 minutes. METHODS: A retrospective audit of 730 stroke charts from 2008 - 2011 was conducted at Health Sciences Centre. 158 patients treated with IV rt-PA were identified. The time intervals between Emergency Department (ED) arrival, administration of rt-PA and uninfused brain computed axial tomographic scan (CT) were recorded. From this, CT to needle times were calculated. During November 2010 to January 2011 feedback was given to neurologists, ED physicians, ED nurses, and CT technologists. This raised awareness and emphasized the importance of this time driven protocol. RESULTS: The median DTN times for 2008, 2009, and 2010 were 69, 71 and 76 minutes respectively. The median CT-to-needle time for this time period was 47 minutes. In 2011 (n =58) the median DTN time was 49 minutes and the median CT-to-needle was 18 minutes, which were marked improvements (p<0.00005 and p<0.005, respectively). In 2008-2010 only 31% of treated patients (n=100) received rt-PA within 60 minutes, whereas in 2011 this increased to 64%. CONCLUSIONS: Dramatic improvements in DTN times and in the percentage of patients receiving rt-PA treatment within 60 minutes were observed in 2011 after feedback was provided regarding the suboptimal performance. Prior to receiving feedback, DTN times were similar to national median DTN times. All centres administering rt-PA for acute ischemic stroke should monitor their clinical performance and give feedback on a regular basis.
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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 | 0.010 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.000 |
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".