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Hospital volume and mortality for mechanical ventilation of medical and surgical patients: A population-based analysis using administrative data*

2006· letter· en· W1992713010 on OpenAlexaboutno aff
Dale M. Needham, Susan E. Bronskill, Deanna M. Rothwell, William J. Sibbald, Peter J. Pronovost, Andreas Laupacis, Thérèse A. Stukel

Bibliographic record

VenueCritical Care Medicine · 2006
Typeletter
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsnot available
FundersAgency for Healthcare Research and Quality
KeywordsMedicineMechanical ventilationOdds ratioConfidence intervalPopulationIntensive careEmergency medicineRetrospective cohort studyOddsIntensive care medicineInternal medicineLogistic regressionEnvironmental health

Abstract

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OBJECTIVE: In an effort to improve efficiency and quality of care, regionalization of adult critical care services, similar to trauma and neonatal intensive care, has been suggested. However, there is little research to understand if hospitals with higher patient volumes have better outcomes. Our objective is to determine whether hospital volume is associated with improved survival for medical or surgical patients receiving mechanical ventilation. DESIGN: Population-based retrospective cohort study. SETTING: Province of Ontario, Canada. PATIENTS: A total of 13,846 medical and 6,373 surgical patients receiving mechanical ventilation for greater than two consecutive days between 1998 and 2000. INTERVENTIONS: None. MEASUREMENTS: Odds ratio for death within 30 days of initiation of mechanical ventilation was calculated in relation to hospital volume of ventilation. Estimates were adjusted for patient demographics, diagnoses, and urgency status; hospital region and rural location; and accounted for clustering within hospitals. MAIN RESULTS: There was no effect of volume on mortality for surgical patients. After adjustment for clustering, among medical patients, the lowest-volume category (<100 episodes/yr) had a nonsignificant increase in mortality, with an odds ratio (95% confidence interval) of 1.13 (0.87-1.47) compared with the highest-volume category (> or =700 episodes/yr). A post hoc analysis revealed that within the lowest-volume category, the proportion of patients transferred to larger hospitals was 81% for hospitals with <20 episodes/yr and only 32% for hospitals with 20-99 episodes/yr, with odds ratios (95% confidence interval) for mortality of 0.74 (0.49-1.12) and 1.18 (0.90-1.54), respectively, compared with the highest-volume category. CONCLUSIONS: For surgical patients requiring mechanical ventilation for >2 days, hospital volume had no effect on mortality. For medical patients, higher mortality may occur in a subgroup of low-volume hospitals that do not routinely transfer their patients to larger-volume facilities. This finding needs further investigation in a larger-sized study.

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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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 integrity0.0010.000
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 head0.144
GPT teacher head0.443
Teacher spread0.299 · 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

Citations84
Published2006
Admission routes1
Has abstractyes

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