Prognostic value of computed tomography score in patients after extracorporeal cardiopulmonary resuscitation
Bibliographic record
Abstract
Abstract Background We evaluated whether Alberta Stroke Program Early Computed Tomography Score (ASPECTS) with some modifications could be used to predict neurological outcomes in patients after extracorporeal cardiopulmonary resuscitation (ECPR). Methods This was a retrospective, multicenter, observational study of adult unconscious patients who were evaluated by brain computed tomography (CT) within 48 hours after ECPR between May 2010 and December 2016. ASPECTS, bilateral ASPECTS (ASPECTS-b), and modified ASPECTS (mASPECTS) were assessed by ROC curves to predict neurological outcomes. The primary outcome was neurological status upon hospital discharge assessed with the Cerebral Performance Categories (CPC) scale. Results Among 58 unconscious patients, survival to discharge was identified in 25 (43.1%) patients. Of these 25 survivors, 19 (32.8%) had good neurological outcomes (CPC score of 1 or 2). Interrater reliability of CT scores was excellent. Intraclass correlation coefficients of ASPECTS, ASPECTS-b, and mASPECTS were 0.918 (95% CI, 0.865–0.950), 0.918 (95% CI, 0.866–0.951), and 0.915 (95% CI, 0.860–0.949), respectively. The predictive performance of mASPECTS for poor neurological outcome was better than that of ASPECTS or ASPECTS-b (C-statistic for mASPECTS vs. ASPECTS, 0.922 vs. 0.812, p = 0.004; mASPECTS vs. ASPECTS-b, 0.922 vs. 0.818, p = 0.003). A cutoff of 25 for poor neurological outcome had a sensitivity of 84.6% (95% CI, 69.5–94.1%) and a specificity of 89.5% (95% CI, 66.9–98.7%) in mASPECTS. Conclusions mASPECTS might be useful for predicting neurological outcomes in patients after ECPR.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".