Diffuse Ischemia in Noncontrast Computed Tomography Predicts Outcome in Patients in Intensive Care Unit
Bibliographic record
Abstract
PURPOSE: In the intensive care unit (ICU), prognosticating patients who are comatose or defining brain death can be challenging. Currently, the criteria for brain death are clinical supported by paraclinical tests. Noncontrast computed tomography (CT) shows diffuse loss of grey-white differentiation consistent with infarction. We hypothesize that the extent of hypodensity is predictive of poor neurologic outcome or brain death. MATERIALS AND METHODS: A total of 235 consecutive adult patients with cardiac arrest or with serious trauma admitted to ICU in 1 year were studied. Seventy met inclusion criteria. CT images were reviewed by multiple observers blinded to final outcome who assessed for loss of grey-white conspicuity. A modification of the validated Alberta Stroke Program Early CT Score (ASPECTS) was used to include non-middle cerebral artery territories. Primary outcome was death or functional disability at 3 months. Dichotomized CT scores were correlated with poor clinical status (Glasgow Coma Score < 5 and APACHE [Acute Physiology and Chronic Health Evaluation] score >19) and poor outcome (modified Rankin Scale >2). RESULTS: The CT score was ≤10 in 7 patients and >10 in 63 patients. The CT score value correlated with the severity of baseline clinical status on the Glasgow Coma Score (r = 0.53, P < .01) and negatively with the APACHE-II score (r = -0.27, P < .05). The CT score value negatively correlated with functional outcome (r = -0.40, P < .01). All the patients with a CT score ≤10 died. The sensitivity of the CT score for functional outcome was 24%, and specificity was 100%. Agreement among observers for the CT score was good (Intraclass correlation coefficient = 0.77). CONCLUSION: Diffuse loss of grey-white matter differentiation is subtle but specific for poor neurologic outcome, which may allow earlier prognostication of patients in whom clinical parameters are difficult to assess.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".