Abstract WP332: Early Stroke Detection in Postoperative Patients: A Simple Educational Intervention and Feasibility Study
Bibliographic record
Abstract
Background: Stroke is one of the most feared postoperative complications. However, the diagnosis is usually delayed leading to a reduced therapeutic window. Objective: To develop a protocol aimed to shorten the time of detecting a neurological deficit in postoperative patients. Methods: We used a pre-post intervention design to evaluate time of stroke recognition in post-opened heart surgery patients. The intervention consisted of: 1) A new postoperative protocol to evaluate any new neurological deficit < 14 days after surgery. Nurses were trained to perform a simple sequential neurological assessment including observing eye deviation and testing for asymmetrical limb movement. The protocol was applied during the vital sign measurement schedule. Stroke code was activated if the deficit was confirmed by MD. 2) Educational program for nurses concerning postoperative stroke complications and the importance of time. Results: During Jan, 2014 - Oct, 2015, we consecutively reviewed 27 patients with acute neurological deficit < 14 days after surgery. Twenty-five consecutive patients with postoperative neurological deficit were enrolled during post-intervention period (Nov, 2015 - Jul, 2016). Male consisted of 19/27 (70.4%) and 179/297 (60.3%) ( P =0.303), the mean ± SD age of 64.56 ± 13.13 and 62.43 ± 13.45 years ( P =0.432) and the mean initial NIHSS of 16.25 ± 8.45 and 12.92 ± 10.78 ( P =0.402) in pre and post intervention respectively. When comparing between pre and post intervention, we found that stroke fast track activation was significantly increased from 4/27(14.8%) to 13/25(52%) ( P =0.004), the median (min-max) duration from time last seen normal to first neurological deficit detection reduced from 690 (20-9190) to 120 (5-5935) minutes ( P =0.004), the median duration from onset to CT decreased from 150 (33-11009) to 65 (31-3779) minutes ( P =0.163). Number needed to treat for early detection when the protocol being used was 2.4. There was an increasing trend in endovascular treatment from 0 to 2/25 (8%) after the intervention. Conclusions: A simple protocol for a postoperative neurological assessment after cardiac surgery showed a significant reduction in the time to diagnosis thus, increasing the patients’ therapeutic opportunity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".