Telephone-based evaluation of cognitive impairment and mood disorders in cardiac arrest survivors with good neurologic outcomes: a retrospective cohort study
Bibliographic record
Abstract
This study determined the occurrence of cognitive impairment and mood disorders in out-of-hospital cardiac arrest (OHCA) survivors with good neurologic outcomes. We performed a retrospective, cross-sectional, single-center study with a total of 97 patients. We evaluated cognitive dysfunction via the Montreal Cognitive Assessment and Alzheimer's disease-8 mood disorders via the Patient Health Questionnaire-9 and the Hospital Anxiety and Depression Scale. We measured quality of life with the European Quality of Life 5-Dimension 5-Levels questionnaire. Cognitive impairment and mood disorders were common among patients with good neurologic recovery. There were 23 patients who experienced cognitive impairments (23.7%) and 28 who suffered from mood disorders (28.9%). Age (adjusted OR 1.07, 95% CI 1.02-1.12), mood disorders (adjusted OR 22.80, 95% CI 4.84-107.49) and hospital length of stay (adjusted OR 1.05, 95% CI 1.02-1.09) were independent risk factors for cognitive impairment. The occurrence of cognitive impairments (adjusted OR 9.94, 95% CI 2.83-35.97) and non-cardiac causes of cardiac arrest (adjusted OR 11.51, 95% CI 3.15-42.15) were risk factors for mood disorders. Quality of life was significantly lower in the OHCA survivors with each disorder than the healthy individuals. Routine screening and intervention are needed for OHCA survivors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".