Abstract W P363: Improvement Door-to-Needle Time For Iv Tpa By Initiation Of Treatment On CT Table
Bibliographic record
Abstract
Background: Rambam Medical Center is the biggest hospital in northern Israel, serving an estimated population of 2,000,000 people. Every year there are as much as 1000 patients hospitalized in Rambam with the diagnosis of acute ischemic stroke. Rambam hospital has facilities for giving all possible types of fibrinolytic treatment in patients with acute ischemic stroke. Methods: We use a special algorithm looking for immediate identification of acute ischemic stroke admitting to ER and then a special neurology nursing stroke team (in cooperation with stroke neurologist) provides fast and complete diagnostic work-up, including imaging battery. Since June 2013 we practice to start a thrombolytic treatment at CT department on CT table immediately after regular brain CT is completed. Results: Using the current approach the minimal time of door-to-needle was reduced to 27 minutes in 2014 as compared with minimal time of 43 minutes in 2012 and 46 minutes in 2011. Conclusions: Start of IV Tpa treatment on CT table significantly reduces door-to-needle time for acute stroke patients. Use of such protocol requires dedicated stroke neurology nursing team.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".