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Variation in length of intensive care unit stay after cardiac arrest: Where you are is as important as who you are

2007· article· en· W2000989443 on OpenAlexaffabout
Sean Keenan, Peter Dodek, Claudio M. Martin, Fran Priestap, Monica Norena, Hubert Wong

Bibliographic record

VenueCritical Care Medicine · 2007
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsSt. Paul's HospitalCentre for Advancing Health OutcomesUniversity of British Columbia
Fundersnot available
KeywordsMedicineGlasgow Coma ScaleIntensive care unitResuscitationRetrospective cohort studyEmergency medicineIntensive careCardiopulmonary resuscitationCohortCohort studyCoronary care unitIntensive care medicineInternal medicineAnesthesiaMyocardial infarction

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine whether hospital site is independently associated with length of intensive care unit (ICU) stay in those patients who die in hospital after experiencing a cardiac arrest. DESIGN: Retrospective cohort study. SETTING: Thirty-one Canadian ICUs, all but one being members of the Critical Care Research Network. PATIENTS: All patients admitted to these ICUs after resuscitation from a cardiac arrest. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Retrospective analysis of prospectively collected clinical data. Using gamma regression with ICU length of stay as the dependent variable, we found the following variables to be independently associated with ICU length of stay: age, gender, Acute Physiology and Chronic Health Evaluation II score, Glasgow Coma Scale score, hospital size, and hospital site. CONCLUSIONS: In this cohort of patients admitted to ICU after cardiac arrest, hospital site was strongly associated with ICU length of stay after controlling for patient-specific factors. Variation in processes of care among ICUs may point to opportunities for improvement.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.364
Teacher spread0.322 · 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 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

Citations79
Published2007
Admission routes2
Has abstractyes

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