From Coma to Cognitive Recovery:Predicting Long-term Outcomes after Cardiac Arrest
Bibliographic record
Abstract
Brain injury after cardiac arrest remains a major challenge. While survival rates have improved, recovery is far from guaranteed: many patients never regain consciousness, and those who do often face lasting cognitive or emotional difficulties. Reliable prognostication and early identification of rehabilitation needs are therefore crucial to improve both treatment decisions and long-term outcomes. The first part of this thesis focused on comatose patients. We showed that somatosensory evoked potentials (SSEP) and electroencephalography (EEG) provide complementary prognostic information. Absent or very low SSEP amplitudes, as well as malignant EEG patterns, were invariably associated with poor outcome. Their combination increased sensitivity without loss of specificity, supporting their role in multimodal prognostication. The second part examined predictors of long-term outcome. Simple bedside measures such as the Montreal Cognitive Assessment (MoCA) and Hospital Anxiety and Depression Scale (HADS) during hospital admission were already associated with cognition and emotional wellbeing at one year. Quantitative EEG features, including peak frequency and alpha-to-theta ratio, also showed associations with later memory performance, though further validation is needed before clinical use. Finally, our sleep study revealed a high prevalence of disorders, such as obstructive sleep apnea, that were linked to cognitive impairment but often underreported in questionnaires. This highlights sleep as a potential target for intervention in survivors. This thesis contributes to the field by improving acute-phase prognostication and exploring potential predictors of long-term recovery after cardiac arrest. By integrating EEG, SSEP, bedside screening, and sleep assessment, it provides a foundation for more personalized prognostication and care, aiming to shift the focus from survival alone to meaningful 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".