Computed tomography perfusion assessment of poor neurological outcome in comatose cardiac arrest patients (CANCCAP): a prospective study
Bibliographic record
Abstract
BACKGROUND: Computed tomography perfusion (CTP) of the brain, are increasingly being employed for the assessment of critically ill patients admitted to intensive care units (ICU), including comatose cardiac arrest patients (CCAP). The purpose of our study was to validate the use of CTP in predicting in-hospital mortality in CCAPs. METHOD: This prospective cohort study enrolled newly admitted adult CCAP, with an out of hospital cardiac arrest (OHCA) and were scheduled for admission to the ICU for further management. Just before ICU admission, CCAP underwent a routine CT scan of the head and CTP of whole head. The treating physicians remained blinded to the CTP results and all patients received standard management. The CTP maps were evaluated to determine a binary outcome of non-survivable brain injury (NSBI), by two independent neuroradiologists, blinded to each other's assessment and to the clinical history of the patients. RESULTS: and lower pH, carbonate, and heart rate. In multivariate analysis, PCI was independently associated with reduction in-hospital mortality. CTP demonstrated exceptionally high specificity (100%; 95% CI 92-100%) and positive predictive value (100%; 95%CI 6.3-100%) for the prediction of NSBI. For CTP, Bennet's S-score showed excellent agreement between the two readers (s = 0.82-0.95). CONCLUSION: CTP was safe and demonstrated very high specificity and positive predictive value and may be used as an additional diagnostic tool for identifying patients at high risk of in-hospital mortality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".