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Record W2106542883 · doi:10.1001/jamasurg.2013.3566

Failure to Rescue in Safety-Net Hospitals

2014· article· en· W2106542883 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 Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineMedicaidSafety netEmergency medicinePatient safetyRetrospective cohort studyMultivariate analysisCohortHealth careMedical emergencyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

IMPORTANCE: Failure to rescue (FTR), the mortality rate among surgical patients with complications, is an emerging quality indicator. Hospitals with a high safety-net burden, defined as the proportion of patients covered by Medicaid or uninsured, provide a disproportionate share of medical care to vulnerable populations. Given the financial strains on hospitals with a high safety-net burden, availability of clinical resources may have a role in outcome disparities. OBJECTIVES: To assess the association between safety-net burden and FTR and to evaluate the effect of clinical resources on this relationship. DESIGN, SETTING, AND PARTICIPANTS: A retrospective cohort of 46,519 patients who underwent high-risk inpatient surgery between January 1, 2007, and December 31, 2010, was assembled using the Nationwide Inpatient Sample. Hospitals were divided into the following 3 safety-net categories: high-burden hospitals (HBHs), moderate-burden hospitals (MBHs), and low-burden hospitals (LBHs). Bivariate and multivariate analyses controlling for patient, procedural, and hospital characteristics, as well as clinical resources, were used to evaluate the relationship between safety-net burden and FTR. MAIN OUTCOMES AND MEASURES: FTR. RESULTS: Patients in HBHs were younger (mean age, 65.2 vs 68.2 years; P = .001), more likely to be of black race (11.3% vs 4.2%, P < .001), and less likely to undergo an elective procedure (39.3% vs 48.6%, P = .002) compared with patients in LBHs. The HBHs were more likely to be large, major teaching facilities and to have high levels of technology (8.6% vs 4.0%, P = .02), sophisticated internal medicine (7.7% vs 4.3%, P = .10), and high ratios of respiratory therapists to beds (39.7% vs 21.1%, P < .001). However, HBHs had lower proportions of registered nurses (27.9% vs 38.8%, P = .02) and were less likely to have a positron emission tomographic scanner (15.4% vs 22.0%, P = .03) and a fully implemented electronic medical record (12.6% vs 17.8%, P = .03). Multivariate analyses showed that HBHs (adjusted odds ratio, 1.35; 95% CI, 1.19-1.53; P < .001) and MBHs (adjusted odds ratio, 1.15; 95% CI, 1.05-1.27; P = .005) were associated with higher odds of FTR compared with LBHs, even after adjustment for clinical resources. CONCLUSIONS AND RELEVANCE: Despite access to resources that can improve patient rescue rates, HBHs had higher odds of FTR, suggesting that availability of hospital clinical resources alone does not explain increased FTR rates.

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)0.001
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 score0.042
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.032
GPT teacher head0.292
Teacher spread0.260 · 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