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Association of Intensive Care Unit Patient-to-Intensivist Ratios With Hospital Mortality

2017· article· en· W2582164904 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJAMA Internal Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsUniversity of TorontoUniversity Health NetworkUniversity of Manitoba
Fundersnot available
KeywordsIntensivistMedicineInterquartile rangeIntensive care unitOdds ratioEmergency medicineLogistic regressionIntensive careStaffingMortality rateOddsIntensive care medicineInternal medicineNursing

Abstract

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Importance: The patient-to-intensivist ratio (PIR) across intensive care units (ICUs) is not standardized and the association of PIR with patient outcome is not well established. Understanding the impact of PIR on outcomes is necessary to optimize senior medical staffing and deliver high-quality care. Objective: To test the hypotheses that: (1) there is significant variation in the PIR across ICUs and (2) higher PIRs are associated with higher hospital mortality for ICU patients. Design, Setting, and Participants: Retrospective cohort analysis of patients (≥16 years) admitted to ICUs staffed by a single intensivist during daytime hours in the United Kingdom from 2010 to 2013. Exposures: Patient-to-intensivist ratios, which we defined for each patient as the number of patients cared for by the intensivist each day averaged over the patient's stay. Main Outcomes and Measures: Using standard summary statistics, we evaluated PIR variation across ICUs. We used multivariable, mixed-effect, logistic regression analysis to evaluate the association between PIR and hospital mortality at ultimate discharge from acute hospital (primary outcome) and at ICU discharge. Finding: Among 49 686 adults in 94 ICUs, median age was 66 (interquartile range [IQR], 52-76) years, and 45.1% were women. The ultimate hospital mortality was 25.7%. The median PIR for patients was 8.5 (IQR, 6.9-10.8; full range, 1.0-23.5), and varied substantially among individual ICUs. The association between PIR and ultimate hospital mortality was U-shaped; there was a reduction in the odds of mortality associated with an increasing PIR up to 7.5 after which the odds of mortality increased again significantly (average patient mortality for lowest PIR, 22%; PIR of 7.5, 15%; highest PIR, 19%; P = .003). A similar U-shaped association was seen for PIR and mortality in the ICU (nadir of mortality at a PIR of 7.8, P < .001). Conclusions and Relevance: PIR varied across UK ICUs. The optimal PIR in this cohort of UK ICU patients was 7.5, with significantly increased ICU and hospital mortality above and below this ratio. The number of patients cared for by 1 intensivist may impact patient outcomes.

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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)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.033
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
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)0.0000.000

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 head0.050
GPT teacher head0.348
Teacher spread0.298 · 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