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Record W2038768340 · doi:10.1177/175114371301400405

Effect of Day and Time of Admission on Mortality in an Intensive Care Unit

2013· article· en· W2038768340 on OpenAlexaff
Jan O. Jansen, Graeme MacLennan, Brian H. Cuthbertson

Bibliographic record

VenueJournal of the Intensive Care Society · 2013
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineIntensive care unitEveningCase mix indexHospital admissionEmergency medicineOddsRetrospective cohort studyOdds ratioCohort studyCohortLogistic regressionIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Variations in outcome relating to out-of-hours care have received considerable attention. We conducted a retrospective cohort study to determine the effect of day and time of admission on mortality in an intensive care unit (ICU) with representative admission severity of illness. Data pertaining to 4,183 patients admitted between 2000 and 2007 were extracted from a prospectively maintained database. Case-mix adjustment was undertaken using the UK APACHE II probability of hospital death. The mean APACHE score was 20.9 with a median predicted hospital mortality of 32.5%. Actual hospital mortality was 30.8%. Compared with Wednesdays as the reference day, admission to ICU on any other given day was not associated with higher crude or case-mix adjusted mortality. Admission to ICU in the evening, compared with daytime admission, was associated with lower odds of crude hospital mortality, but this difference was no longer significant after case-mix adjustment. Case-mix adjusted in-hospital mortality does not vary with day and time of admission, even in patients with higher severity of illness and higher predicted mortality than previously reported.

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)0.001
metaresearch head score (Gemma)0.009
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.016
GPT teacher head0.322
Teacher spread0.306 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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