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Record W1965186025 · doi:10.1097/mlr.0b013e3181792525

Health Impact of Hospital Restrictions on Seriously Ill Hospitalized Patients

2008· article· en· W1965186025 on OpenAlexaffabout
Thérèse A. Stukel, Michael J. Schull, Astrid Guttmann, David A. Alter, Ping Li, Marian J. Vermeulen, Douglas G. Manuel, Merrick Zwarenstein

Bibliographic record

VenueMedical Care · 2008
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineMEDLINEIntensive care medicineEmergency medicinePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Restrictions on non-urgent hospital care imposed to control the 2003 Toronto severe acute respiratory syndrome outbreak led to substantial disruptions in hospital clinical practice, admission, and transfer patterns. OBJECTIVES: We assessed whether there were unintended health consequences to seriously ill hospitalized patients. STUDY DESIGN, SETTING, AND POPULATION: Population-based longitudinal cohort study of patients residing in Toronto or an urban control region with an incident admission for 1 of 7 serious conditions in the 3 years before, or the 4 months during or after restrictions. OUTCOME MEASURES: Short-term mortality, overall readmissions, cardiac readmissions for acute myocardial infarction patients, serious complications for very low birth weight babies, and quality of care measures, comparing adjusted rates across time periods within regions. RESULTS: Mortality, readmission, and complication rates did not change for any condition during or after severe acute respiratory syndrome restrictions. Although rates of invasive cardiac procedures for acute myocardial infarction patients decreased 11-37% in Toronto, rates of nonfatal cardiac outcomes did not change. CONCLUSIONS: Restrictions on non-urgent hospital utilization and hospital transfers may be a safe public health strategy to employ to control nosocomial outbreaks or provide hospital surge capacity for up to several months, in large, well-developed healthcare systems with good availability of community-based care.

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)0.000
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 score0.022
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.033
GPT teacher head0.352
Teacher spread0.320 · 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.

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

Citations24
Published2008
Admission routes2
Has abstractyes

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