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Record W2059294586 · doi:10.1097/ccm.0b013e318292313a

Do Intensivist Staffing Patterns Influence Hospital Mortality Following ICU Admission? A Systematic Review and Meta-Analyses*

2013· review· en· W2059294586 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCritical Care Medicine · 2013
Typereview
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsLakeridge HealthQueen's UniversityUniversity Health Network
FundersLondon School of Hygiene and Tropical Medicine
KeywordsIntensivistMedicineStaffingIntensive care unitEmergency medicineObservational studyStandardized mortality ratioMeta-analysisIntensive care medicineMortality rateInternal medicineNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the effect of different intensivist staffing models on clinical outcomes for critically ill patients. DATA SOURCES: A sensitive search of electronic databases and hand-search of major critical care journals and conference proceedings was completed in October 2012. STUDY SELECTION: Comparative observational studies examining intensivist staffing patterns and reporting hospital or ICU mortality were included. DATA EXTRACTION: Of 16,774 citations, 52 studies met the inclusion criteria. We used random-effects meta-analytic models unadjusted for case-mix or cluster effects and quantified between-study heterogeneity using I. Study quality was assessed using the Newcastle-Ottawa Score for cohort studies. DATA SYNTHESIS: High-intensity staffing (i.e., transfer of care to an intensivist-led team or mandatory consultation of an intensivist), compared to low-intensity staffing, was associated with lower hospital mortality (risk ratio, 0.83; 95% CI, 0.70-0.99) and ICU mortality (pooled risk ratio, 0.81; 95% CI, 0.68-0.96). Significant reductions in hospital and ICU length of stay were seen (-0.17 d, 95% CI, -0.31 to -0.03 d and -0.38 d, 95% CI, -0.55 to -0.20 d, respectively). Within high-intensity staffing models, 24-hour in-hospital intensivist coverage, compared to daytime only coverage, did not improved hospital or ICU mortality (risk ratio, 0.97; 95% CI, 0.89-1.1 and risk ratio, 0.88; 95% CI, 0.70-1.1). The benefit of high-intensity staffing was concentrated in surgical (risk ratio, 0.84; 95% CI, 0.44-1.6) and combined medical-surgical (risk ratio, 0.76; 95% CI, 0.66-0.83) ICUs, as compared to medical (risk ratio, 1.1; 95% CI, 0.83-1.5) ICUs. The effect on hospital mortality varied throughout different decades; pooled risk ratios were 0.74 (95% CI, 0.63-0.87) from 1980 to 1989, 0.96 (95% CI, 0.69-1.3) from 1990 to 1999, 0.70 (95% CI, 0.54-0.90) from 2000 to 2009, and 1.2 (95% CI, 0.84-1.8) from 2010 to 2012. These findings were similar for ICU mortality. CONCLUSIONS: High-intensity staffing is associated with reduced ICU and hospital mortality. Within a high-intensity model, 24-hour in-hospital intensivist coverage did not reduce hospital, or ICU, mortality. Benefits seen in mortality were dependent on the type of ICU and decade of publication.

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.

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)0.001
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.510
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0140.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.321
GPT teacher head0.519
Teacher spread0.198 · 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