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Record W2096258732 · doi:10.1002/jhm.2025

Influence of neighborhood household income on early death or urgent hospital readmission

2013· article· en· W2096258732 on OpenAlexaff
Carl van Walraven, Jenna Wong, Alan J. Forster

Bibliographic record

VenueJournal of Hospital Medicine · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsInstitute for Clinical Evaluative SciencesOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineSocioeconomic statusComorbidityHospital medicineHousehold incomeEmergency medicineEmergency departmentPopulationHospital admissionHealth careCharlson comorbidity indexDemographyEnvironmental healthFamily medicineInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The relationship of socioeconomic status (SES) with hospital readmissions is unclear. METHODS: We used population-based administrative datasets to randomly select 40,827 adult Ontarians discharged from hospital to the community. Patient postal codes were linked to average neighborhood household-income quintiles. The association of this SES measure with 30-day death or urgent readmission was measured after controlling for outcome risk using a validated index, LACE+: length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson Comorbidity Index score (C), and emergency-department use (E). RESULTS: Within 1 month of discharge, 2638 (6.5%) people died or were urgently readmitted. Lower neighborhood income was significantly associated with both an increased outcome risk (P < 0.0001) and LACE+ score. After adjusting for LACE+ score, neighborhood income was no longer associated with 30-day death or urgent readmission (P = 0.21). CONCLUSIONS: After accounting for known risk factors, early death or readmission is not more common in people from lower-income neighborhoods. Further study is required to determine if SES is associated with adverse postdischarge outcomes in settings without publicly funded healthcare.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.034
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.024
GPT teacher head0.317
Teacher spread0.293 · 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

Citations21
Published2013
Admission routes1
Has abstractyes

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