MétaCan
Menu
Back to cohort
Record W2941682643 · doi:10.1097/mcc.0000000000000607

Assessment of neurocognitive function after cardiac arrest

2019· review· en· W2941682643 on OpenAlexaboutno aff
Erik Nordström, Gisela Lilja

Bibliographic record

VenueCurrent Opinion in Critical Care · 2019
Typereview
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsnot available
Fundersnot available
KeywordsNeurocognitiveMedicineCognitionCardiac monitoringIntensive care medicinePsychiatryCardiology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Impaired neurocognitive function is common in cardiac arrest survivors and the use of specific neurocognitive assessments are recommended in both clinical trials and daily practice. This review examines the most recent evidence to guide in the selection of neurocognitive outcome assessment tools after cardiac arrest. RECENT FINDINGS: Neurocognitive impairment after cardiac arrest was recently reported as one of the major predictors for societal participation, highlighting the need for neurocognitive assessments. A subjective report is a simple method to screen for cognitive problems, but divergent findings were reported when comparing with objective measures. A standardized observer report may be useful for cognitive screening postcardiac arrest. The Montreal Cognitive Assessment (MoCA) was recommended for cognitive screening after cardiac arrest. Detailed neurocognitive assessments were reported as valuable for in-depth evaluation of patients in interventional studies. The best time-point for neurocognitive assessments remains unknown. Recent findings report that most neurocognitive recovery is seen within the first months after cardiac arrest, with some improvement also noted between 3 and 12 months postcardiac arrest. SUMMARY: Neurocognitive assessments after cardiac arrest are important and the approach should differ depending on the clinical situation. Large, prospective, well designed studies, to guide the selection of neurocognitive assessments after cardiac arrest, are urgently needed.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.914
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.158
GPT teacher head0.493
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations20
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Opinion in Critical CareSame topicCardiac Arrest and ResuscitationFrench-language works237,207