Telephone-based evaluation of cognitive impairment and mood disorders among out-of- hospital cardiac arrest survivors with good neurologic outcome: a retrospective cohort study
Bibliographic record
Abstract
Abstract This study determined the incidence of the cognitive impairments and mood disorders by telephone-based evaluation using previously mentioned scoring systems in out-of-hospital cardiac arrest (OHCA) survival with good neurologic outcome. Retrospective, cross-sectional, single-center study was performed, and a total of 97 patients were analyzed. Telephone version of Montreal Cognitive Assessment, Alzheimer’s disease-8 were used for evaluating cognitive dysfunctions, and the Patient Health Questionnaire-9 and the Hospital Anxiety and Depression Scale were used for assessing mood disorders. Quality of life was measured with the European Quality of Life 5-Dimension 5-Levels questionnaire. About one fourth patients experienced cognitive impairments (n = 23, 23.7%) or mood disorders (n = 28, 28.9%). Combined mood disorders (adjusted OR 21.36, 95% CI 5.14–88.84) and hospital length of stay (adjusted OR 1.04, 95% CI 1.01–1.08) were independent risk factors. In case of mood disorders, combined cognitive impairments (adjusted OR 9.94, 95% CI 2.83–35.97) and non-cardiac cause of cardiac arrest (adjusted OR 11.51, 95% CI 3.15–42.15) were risk factors. Furthermore, the quality of life was significantly low in the group with both cognitive impairments and mood disorders. Cognitive impairments and mood disorders were common among patients with good neurologic recovery.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".