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Record W2092760657 · doi:10.1001/jama.291.7.870

Is This Patient Dead, Vegetative, or Severely Neurologically Impaired?

2004· review· en· W2092760657 on OpenAlexaff
Christopher M. Booth, Robert Boone, George Tomlinson, Allan S. Detsky

Bibliographic record

VenueJAMA · 2004
Typereview
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineComa (optics)Confidence intervalPhysical examinationNeurological examinationResuscitationMEDLINEReflexEmergency medicineIntensive care medicinePediatricsAnesthesiaInternal medicineSurgery

Abstract

fetched live from OpenAlex

CONTEXT: Most survivors of cardiac arrest are comatose after resuscitation, and meaningful neurological recovery occurs in a small proportion of cases. Treatment can be lengthy, expensive, and often difficult for families and caregivers. Physical examination is potentially useful in this clinical scenario, and the information obtained may help physicians and families make accurate decisions about treatment and/or withdrawal of care. OBJECTIVE: To determine the precision and accuracy of the clinical examination in predicting poor outcome in post-cardiac arrest coma. DATA SOURCES AND STUDY SELECTION: We searched MEDLINE for English-language articles (1966-2003) using the terms coma, cardiac arrest, prognosis, physical examination, sensitivity and specificity, and observer variation. Other sources came from bibliographies of retrieved articles and physical examination textbooks. Studies were included if they assessed the precision and accuracy of the clinical examination in prognosis of post-cardiac arrest coma in adults. Eleven studies, involving 1914 patients, met our inclusion criteria. DATA EXTRACTION: Two authors independently reviewed each study to determine eligibility, abstract data, and classify methodological quality using predetermined criteria. Disagreement was resolved by consensus. DATA SYNTHESIS: Summary likelihood ratios (LRs) were calculated from random effects models. Five clinical signs were found to strongly predict death or poor neurological outcome: absent corneal reflexes at 24 hours (LR, 12.9; 95% confidence interval [CI], 2.0-68.7), absent pupillary response at 24 hours (LR, 10.2; 95% CI, 1.8-48.6), absent withdrawal response to pain at 24 hours (LR, 4.7; 95% CI, 2.2-9.8), no motor response at 24 hours (LR, 4.9; 95% CI, 1.6-13.0), and no motor response at 72 hours (LR, 9.2; 95% CI, 2.1-49.4). The proportion of individuals' dying or having a poor neurological outcome was calculated by pooling the outcome data from the 11 studies (n = 1914) and used as an estimate of the pretest probability of poor outcome. The random effects estimate of poor outcome was 77% (95% CI, 72%-80%). The highest LR increases the pretest probability of 77% to a posttest probability of 97% (95% CI, 87%-100%). No clinical findings were found to have LRs that strongly predicted good neurological outcome. CONCLUSIONS: Simple physical examination maneuvers strongly predict death or poor outcome in comatose survivors of cardiac arrest. The most useful signs occur at 24 hours after cardiac arrest, and earlier prognosis should not be made by clinical examination alone. These data provide prognostic information, rather than treatment recommendations, which must be made on an individual basis incorporating many other variables.

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)0.021
metaresearch head score (Gemma)0.128
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 score0.021
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.128
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0050.008
Science and technology studies0.0010.003
Scholarly communication0.0030.004
Open science0.0020.001
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.043
GPT teacher head0.330
Teacher spread0.288 · 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

Citations509
Published2004
Admission routes1
Has abstractyes

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