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Record W2076708288 · doi:10.1097/ccm.0000000000000335

Predicting Neurologic Outcome After Targeted Temperature Management for Cardiac Arrest

2014· review· en· W2076708288 on OpenAlexafffund
Eyal Golan, Kali Barrett, Aziz S. Alali, Abhijit Duggal, Draga Jichici, Ruxandra Pinto, Laurie J. Morrison, Damon C. Scales

Bibliographic record

VenueCritical Care Medicine · 2014
Typereview
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsInstitute for Clinical Evaluative SciencesHealth Sciences CentreSunnybrook Health Science CentreMcMaster UniversityUniversity of TorontoSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineLikelihood ratios in diagnostic testingTargeted temperature managementGlasgow Coma ScaleComa (optics)False positive rateInternal medicineMeta-analysisReturn of spontaneous circulationAnesthesiaCardiopulmonary resuscitationResuscitation

Abstract

fetched live from OpenAlex

OBJECTIVES: Targeted temperature management improves survival and neurologic outcomes for adult out-of-hospital cardiac arrest survivors but may alter the accuracy of tests for predicting neurologic outcome after cardiac arrest. DATA SOURCES: We systematically searched Medline, Embase, CINAHL, and CENTRAL from database inception to September 2012. STUDY SELECTION: Citations were screened for studies that examined diagnostic tests to predict poor neurologic outcome or death following targeted temperature management in adult cardiac arrest survivors. DATA EXTRACTION: Data on study outcomes and quality were abstracted in duplicate. We constructed contingency tables for each diagnostic test and calculated sensitivity, specificity, and positive and negative likelihood ratios. DATA SYNTHESIS: Of 2,737 citations, 20 studies (n = 1,845) met inclusion criteria. Meta-analysis showed that three tests accurately predicted poor neurologic outcome with low false-positive rates: bilateral absence of pupillary reflexes more than 24 hours after a return of spontaneous circulation (false-positive rate, 0.02; 95% CI, 0.01-0.06; summary positive likelihood ratio, 10.45; 95% CI, 3.37-32.43), bilateral absence of corneal reflexes more than 24 hours (false-positive rate, 0.04; 95% CI, 0.01-0.09; positive likelihood ratio, 6.8; 95% CI, 2.52-18.38), and bilateral absence of somatosensory-evoked potentials between days 1 and 7 (false-positive rate, 0.03; 95% CI, 0.01-0.07; positive likelihood ratio, 12.79; 95% CI, 5.35-30.62). False-positive rates were higher for a Glasgow Coma Scale motor score showing extensor posturing or worse (false-positive rate, 0.09; 95% CI, 0.06-0.13; positive likelihood ratio, 7.11; 95% CI, 5.01-10.08), unfavorable electroencephalogram patterns (false-positive rate, 0.07; 95% CI, 0.04-0.12; positive likelihood ratio, 8.85; 95% CI, 4.87-16.08), myoclonic status epilepticus (false-positive rate, 0.05; 95% CI, 0.02-0.11; positive likelihood ratio, 5.58; 95% CI, 2.56-12.16), and elevated neuron-specific enolase (false-positive rate, 0.12; 95% CI, 0.06-0.23; positive likelihood ratio, 4.14; 95% CI, 1.82-9.42). The specificity of available tests improved when these were performed beyond 72 hours. Data on neuroimaging, biomarkers, or combination testing were limited and inconclusive. CONCLUSION: Simple bedside tests and somatosensory-evoked potentials predict poor neurologic outcome for survivors of cardiac arrest treated with targeted temperature management, and specificity improves when performed beyond 72 hours. Clinicians should use caution with these predictors as they carry the inherent risk of becoming self-fulfilling.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.064
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.007
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.363
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations97
Published2014
Admission routes2
Has abstractyes

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