Neuroprognostication After Cardiac Arrest
Bibliographic record
Abstract
Cardiac arrest is a significant cause of mortality and morbidity. Despite advances in technologies and resuscitative care, patients who remain comatose after cardiac arrest present the bedside clinician with both diagnostic and therapeutic uncertainty because of variable comfort with how best to neuroprognosticate. Recent guidelines attempt to address existing knowledge gaps; however, significant variability remains in clinical practice, including the application of guideline recommendations at the bedside. We present a case-based discussion to illustrate key principles for early care and a subsequent approach to neuroprognostication. We explore many of the clinical nuances in neuroprognostication, including the utility of the clinical examination combined with either neuroimaging or neurophysiologic studies, in helping to care for these patients and support their families in decision-making processes. We discuss how a multimodal approach to neuroprognostication may be subject to site-specific availability of testing. Furthermore, how to incorporate the multidisciplinary team in patient care, including subspecialty services such as neurology and palliative care, is discussed when faced with complex clinical situations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".