Abstract 286: Cerebral Microstructure Disruptions are Associated With Poor Neurologic Outcomes in Comatose Cardiac Arrest Patients
Bibliographic record
Abstract
Background: For cardiac arrest survivors who are initially comatose after restoration of spontaneous circulation, the extent of brain injury and expected recovery are crucial for management decisions. Advanced diffusion imaging approaches may provide additional insight into microstructural integrity and potential for arousal recovery (AR). Methods: Multi-shell diffusion imaging was acquired in a prospective study. Neurite orientation dispersion (OD), intracellular volume fraction (ICVF), free water fraction (ISO), mean kurtosis (MK), axial kurtosis (AK), radial kurtosis (RK), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD) and fractional anisotropy (FA) were calculated (see Figure). Median whole-brain values in patients with AR by discharge were compared with those with no AR and to controls (ANOVA, post-hoc 2-tailed Wilcoxon test). Multivariable backward stepwise logistic regression was performed to predict AR. Results: 19 patients (mean ±SD 48±23 y, 42% men) and 5 controls (37±19 y, 40% men) were analyzed (10 patients with AR, 9 without). Median [IQR] time-to-MRI was 5 [4-8] days. Patients with no AR had higher AK (P=0.037), and RK (P=.045) than those with AR. Controls exhibited higher FA than either AR (P=.009) or no AR (P=.02), lower MK than no AR (P=.02), lower AK than no AR (P=.02), lower RK than no AR (P=0.02), lower OD than no AR (P=.008), and lower ICVF than no AR (P=.046). AK (P=.049) and ICVF (P=.01) were found in multivariable regression to be significant predictors of AR with 81% area under the receiver operating curve (AUC). Discussion: Advanced diffusion imaging metrics, AK and ICVF, were able to predict which patients were likely to regain consciousness, with 81% AUC. Despite the small sample size, we note statistically significant differences using more sensitive measures of microstructural injury compared to more commonly used diffusivity metrics. Further investigation of advanced diffusion imaging methods is warranted.
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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 | 0.001 | 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) | 0.003 | 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".