Assessment of neurocognitive function after cardiac arrest
Bibliographic record
Abstract
PURPOSE OF REVIEW: Impaired neurocognitive function is common in cardiac arrest survivors and the use of specific neurocognitive assessments are recommended in both clinical trials and daily practice. This review examines the most recent evidence to guide in the selection of neurocognitive outcome assessment tools after cardiac arrest. RECENT FINDINGS: Neurocognitive impairment after cardiac arrest was recently reported as one of the major predictors for societal participation, highlighting the need for neurocognitive assessments. A subjective report is a simple method to screen for cognitive problems, but divergent findings were reported when comparing with objective measures. A standardized observer report may be useful for cognitive screening postcardiac arrest. The Montreal Cognitive Assessment (MoCA) was recommended for cognitive screening after cardiac arrest. Detailed neurocognitive assessments were reported as valuable for in-depth evaluation of patients in interventional studies. The best time-point for neurocognitive assessments remains unknown. Recent findings report that most neurocognitive recovery is seen within the first months after cardiac arrest, with some improvement also noted between 3 and 12 months postcardiac arrest. SUMMARY: Neurocognitive assessments after cardiac arrest are important and the approach should differ depending on the clinical situation. Large, prospective, well designed studies, to guide the selection of neurocognitive assessments after cardiac arrest, are urgently needed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".