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Clinician predictions of intensive care unit mortality*

2004· article· en· W2058219198 on OpenAlexaff
Graeme Rocker, Peter Sjökvist, Bruce Weaver, Simon Finfer, Ellen McDonald, John C. Marshall, Anne V. Kirby, Mitchell M. Levy, Peter Dodek, Daren K. Heyland, Gordon Guyatt

Bibliographic record

VenueCritical Care Medicine · 2004
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineIntensive care unitIntensive care medicineIntensive care

Abstract

fetched live from OpenAlex

OBJECTIVE: Predicting outcomes for critically ill patients is an important aspect of discussions with families in the intensive care unit. Our objective was to evaluate clinical intensive care unit survival predictions and their consequences for mechanically ventilated patients. DESIGN: Prospective cohort study. SETTING: Fifteen tertiary care centers. PATIENTS: Consecutive mechanically ventilated patients > or = 18 yrs of age with expected intensive care unit stay > or = 72 hrs. INTERVENTIONS: We recorded baseline characteristics at intensive care unit admission. Daily we measured multiple organ dysfunction score (MODS), use of advanced life support, patient preferences for life support, and intensivist and bedside intensive care unit nurse estimated probability of intensive care unit survival. MEASUREMENTS AND MAIN RESULTS: The 851 patients were aged 61.2 (+/- 17.6, mean + SD) yrs with an Acute Physiology and Chronic Health Evaluation (APACHE) II score of 21.7 (+/- 8.6). Three hundred and four patients (35.7%) died in the intensive care unit, and 341 (40.1%) were assessed by a physician at least once to have a < 10% intensive care unit survival probability. Independent predictors of intensive care unit mortality were baseline APACHE II score (hazard ratio, 1.16; 95% confidence interval, 1.08-1.24, for a 5-point increase) and daily factors such as MODS (hazard ratio, 2.50; 95% confidence interval, 2.06-3.04, for a 5-point increase), use of inotropes or vasopressors (hazard ratio, 2.14; 95% confidence interval, 1.66-2.77), dialysis (hazard ratio, 0.51; 95% confidence interval, 0.35-0.75), patient preference to limit life support (hazard ratio, 10.22; 95% confidence interval, 7.38-14.16), and physician but not nurse prediction of < 10% survival. The impact of physician estimates of < 10% intensive care unit survival was greater for patients without vs. those with preferences to limit life support (p < .001) and for patients with less vs. more severe organ dysfunction (p < .001). Mechanical ventilation, inotropes or vasopressors, and dialysis were withdrawn more often when physicians predicted < 10% probability of intensive care unit survival (all ps < .001). CONCLUSIONS: Physician estimates of intensive care unit survival < 10% are associated with subsequent life support limitation and more powerfully predict intensive care unit mortality than illness severity, evolving or resolving organ dysfunction, and use of inotropes or vasopressors.

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)0.003
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.195
GPT teacher head0.466
Teacher spread0.271 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations249
Published2004
Admission routes1
Has abstractyes

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