Don’t lose heart : Neuropsychological outcome and treatment after cardiac arrest
Bibliographic record
Abstract
The number of people experiencing long-term cognitive and psychosocial consequences after out-of-hospital cardiac arrest is increasing due to an aging population and an increased number of successful cardiorespiratory resuscitations. About half of the survivors suffer from long-term cognitive impairment. The thesis explores the diagnosis, the long-term outcomes, and treatment of cognitive impairment post-cardiac arrest. The Montreal Cognitive Assessment (MoCA) was validated, showing excellent sensitivity and adequate specificity for detecting cognitive impairment, and is recommended to use for follow-up assessment. Not all patients with cognitive impairments report cognitive complaints, and vice versa. Our findings did not support a lack of awareness as an explanation for this. Instead, a “response shift”, a change in an individual’s standards and values after life-altering events, is proposed. Epileptiform patterns on EEG occur in 5-10% of comatose patients and indicate severe brain injury. A study of 14 survivors showed that, despite a “good” outcome according to the Cerebral Performance Categories, almost all participants had cognitive impairment and a worse psychosocial outcome compared to other patient populations. Three studies investigated potential treatments. First, intravenous acyl-ghrelin improved cognitive and psychosocial outcomes numerically but not significantly compared to the placebo group. Second, cognitive rehabilitation combining metacognitive strategy training and direct computerized cognitive training in a single case experimental design enhanced daily functioning and societal participation for most participants. Third, intermitted theta burst stimulation (iTBS) had no impact on working memory on an individual basis, but may have influenced reaction time at the group level. This thesis contributes to the field of post cardiac-arrest research by validating a screening tool, improving understanding of long-term outcomes, and identifying potential treatments.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".