Institution of Code Neurointervention and Its Impact on Reaction and Treatment Times.
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Various strategies have been implemented to reduce acute stroke treatment times. Recent studies have shown a significant benefit of acute endovascular therapy. The JFK Comprehensive Stroke Center instituted Code Neurointervention (NI) on May 1, 2014 for the purpose of rapidly assembling the NI team and rapidly providing acute endovascular therapy. DESIGN/METHODS: We performed a retrospective analysis of all patients who had Code NI (Code NI group) called from May 1, 2014 to July 30, 2018 and compared them to patients who underwent acute endovascular treatment prior to initiation of the code (pre-Code NI group) between January 2012 and April 30, 2014. The following parameters were compared: door to puncture (DTP) and door to recanalization (DTR) times, as well as preprocedure NIHSS, 24-hour postprocedure NIHSS, and 90-day modified Rankin scores. RESULTS: =0.036, 0.32-0.96). CONCLUSION: Institution of Code NI significantly improved DTP and DTR times as well as mRS at 3-months postprocedure. Rapid assembly of the NI team, rapid availability of imaging and angiography suite, and streamlining of processes, likely contribute to these differences. These lessons and more widespread institution of such codes will further aid in improving acute stroke care for patients.
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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 | 0.003 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".