CT perfusion for Assessment of poor Neurological outcome in Comatose Cardiac Arrest Patients (CANCCAP): protocol for a prospective study
Bibliographic record
Abstract
INTRODUCTION: Cardiac arrest remains one of the most common causes of death with the majority occurring outside of hospitals (out of hospital cardiac arrest). Despite advancements in resuscitation management, approximately 50% of comatose cardiac arrest patients (CCAP) will suffer a severe unsurvivable brain injury. To assess brain injury, a neurological examination is conducted, however, its reliability in predicting outcomes in the first days following cardiac arrest is limited. Non-contrast CT is the most employed scan to assess hypoxic changes, even though it is not sensitive to early hypoxic-ischaemic changes in the brain. CT perfusion (CTP) has shown high sensitivity and specificity in brain death patients, although its use in predicting poor neurological outcome in CCAP has not yet been explored. The purpose of this study is to validate CTP for predicting poor neurological outcome (modified Rankin scale, mRS≥4) at hospital discharge in CCAP. METHODS AND ANALYSIS: The CT Perfusion for Assessment of poor Neurological outcome in Comatose Cardiac Arrest Patients study is a prospective cohort study funded by the Manitoba Medical Research Foundation. Newly admitted CCAP receiving standard Targeted Temperature Management are eligible. Patients undergo a CTP at the same time as the admission standard of care head CT. Admission CTP findings will be compared with the reference standard of an accepted bedside clinical assessment at the time of admission. Deferred consent will be used. The primary outcome is a binary outcome of good neurological status, defined as mRs<4 or poor neurological status (mRs≥4) at hospital discharge. A total of 90 patients will be enrolled. ETHICS AND DISSEMINATION: This study has been approved by the University of Manitoba Health Research Ethics Board. The findings from our study will be disseminated through peer-reviewed journals and presentations at local rounds, national and international conferences. The public will be informed at the end of the study. TRIAL REGISTRATION NUMBER: NCT04323020.
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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 | 0.041 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".