Multimodal Neuroprognostication of Poor Neurological Outcomes after Cardiac Arrest: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Brain injury related to hypoxic-ischemic insults post-cardiac arrest is a highly morbid and often fatal condition for which neuroprognostication remains challenging. There has been a significant increase in studies assessing the accuracy of multimodal approaches in predicting poor neurological outcomes post-cardiac arrest, and contemporary guidelines recommend this approach. We conducted a systematic review to assess multimodal versus unimodal approaches in neuroprognostication for predicting a poor neurological outcome for adult post-cardiac arrest patients at hospital discharge or beyond. METHODS: PRISMA methodological standards were followed. MEDLINE, EMBASE and CINAHL were searched from inception until January 18, 2024, with no restrictions. Abstract and full-text review was completed in duplicate. Original studies assessing the prognostic accuracy (specificity and false positive rate [FPR]) of multimodal compared with unimodal approaches were included. The risk of bias was assessed using the QUIPS tool. Data were extracted in duplicate. RESULTS: Of 791 abstracts, 12 studies were included. The FPR in predicting poor neurological outcomes ranged from 0% to 5% using a multimodal approach compared to 0% to 31% with a unimodal test. The risk of bias was moderate to high for most components. CONCLUSIONS: A multimodal approach may improve the FPR in predicting poor neurological outcomes of post-cardiac arrest 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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".