Abstract 220: Comparison of 3 Cognitive Exams in Cardiac Arrest Survivors
Bibliographic record
Abstract
Background: Cognitive deficits may detract from quality of life after cardiac arrest (CA). The pattern and prevalence of these deficits are not well documented. We used the Computer Assessment of Mild Cognitive Impairment (CAMCI), the Montreal Cognitive Assessment (MOCA) and the 41 Cent Test to assess cognitive impairment in survivors of CA. We hypothesized that CAMCI subscales and other scores that were highly correlated could identify specific domains of impairment in CA survivors. Methods: Four researchers administered the CAMCI, MOCA, and/or the 41 Cent Test to CA survivors after discharge from the intensive care unit between 2010 and 2014. Physicians screened patients with the Mini-Mental State Exam to determine when this cognitive testing was feasible. We compared the distribution of scores between patients who presented with coma and those who awoke immediately after CA. Pairwise correlations between the different subscales and tests were considered significant with alpha error of 0.05. Results: Ninety-two participants completed the CAMCI, of which 18 participants completed the CAMCI, MOCA and 41 Cent Test. The mean (SD) percentile score for CAMCI was 32.2 (20.3) out of possible 100, for the MOCA was 20.3 (5.2) out of a possible 30 points and the 41 Cent Test was 5.4 (1.1) out of a possible 7 points. MOCA correlated strongly with the overall CAMCI score (r = 0.82) and with the executive accuracy subscale of the CAMCI (r = 0.75). The executive accuracy subscale and overall CAMCI score correlated with one another (r = 0.81) when all 92 CAMCI exams were considered. The MOCA and 41 Cent Test were correlated with each other (r = 0.63). Conclusion: The CAMCI detects cognitive impairment after CA; the MOCA correlates strongly with the overall CAMCI and the executive function subscale of the CAMCI. The 41 Cent Test may not be as effective as the MOCA in detecting cognitive deficits.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".