Brain network changes and cognitive function after cardiac arrest
Bibliographic record
Abstract
Abstract Survival rates after out-of-hospital cardiac arrest have improved over the past two decades. Despite this progress, long-term cognitive impairment remains prevalent even in those with early recovery of consciousness after out-of-hospital cardiac arrest; however, little is known about the determinants and underlying mechanisms. We utilized the REcovery after cardiac arrest surVIVAL cohort of out-of-hospital cardiac arrest survivors who fully regained consciousness to correlate cognition measurements with brain network changes using resting-state functional MRI and the Montreal Cognitive Assessment at hospital discharge and a comprehensive neuropsychological assessment at three-month follow-up. About half of out-of-hospital cardiac arrest survivors displayed cognitive impairments at discharge, and in most, cognitive deficits persisted at three-month follow-up, particularly in the executive and visuospatial functions. Compared to healthy controls, out-of-hospital cardiac arrest survivors exhibited increased connectivity between resting-state networks, particularly involving the frontoparietal network. The increased connectivity between the frontoparietal and visual networks was associated with less favourable cognitive outcomes (β = 14.0, P = 0.01), while higher education seemed to confer some cognitive protection (β = −2.06, P = 0.03). In sum, the data highlight the importance of subtle cognitive impairment, also in out-of-hospital cardiac arrest survivors who are eligible for home discharge, and the potential of functional MRI to identify alterations in brain networks correlating with cognitive outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".