Early prediction of long-term cognitive function, emotional distress, and health-related quality of life in cardiac arrest survivors
Bibliographic record
Abstract
BACKGROUND: Cardiac arrest survivors are at risk of long-term disturbances in cognition, emotional distress, and health-related quality of life (QoL). Early prediction can guide tailored treatment. We evaluated whether early clinical measures predict long-term outcomes. METHODS: In a longitudinal multicenter cohort study, we prospectively included patients who regained consciousness after cardiac arrest. Potential predictors during hospitalization were scores on the Hospital Anxiety and Depression Scale (HADS), Montreal Cognitive Assessment (MoCA), and Barthel Index, and presence of delirium and transient coma >24 h. These were related to cognitive function (MoCA), emotional distress (HADS), and health-related QoL (EQ-5D-5L) at twelve months using multivariate mixed-effects models, with age and sex as covariates. Likelihood ratios were calculated for cognitive impairment (MoCA < 26). RESULTS: We included 100 patients; 20 % showed cognitive impairment at twelve months and 8 % and 10 % showed signs of anxiety and depression, respectively. Early MoCA showed a borderline significant positive relation with twelve-month MoCA (β = 0.21, p = 0.05). The likelihood ratio for early MoCA < 26 predicting cognitive impairment at twelve months was LR+ 1.29 (95 % CI: 0.40-4.20) and LR- 0.60 (95 % CI: 0.18-1.94). Early HADS was associated with twelve-month HADS (β = 0.47, 95 % CI: 0.33-0.60, p < 0.001), with age negatively associated (β = -0.10, 95 % CI: -0.18 to -0.02, p = 0.019). No other significant relations were found. CONCLUSION: MoCA and HADS scores during hospitalization relate to long-term cognitive and emotional outcomes after cardiac arrest. These measures may help identify patients who could benefit from cognitive rehabilitation or psychosocial support.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".