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Record W4311525183 · doi:10.1093/ofid/ofac492.079

887. Race and ethnicity differences in hospital length of stay for children with acute osteomyelitis in the United States

2022· article· en· W4311525183 on OpenAlexaff
Jeffrey I. Campbell, Kristen H. Shanahan, Melissa Bartick, Mohsin Ali, Donald A. Goldmann, Nadia Shaikh, Sophie Allende‐Richter

Bibliographic record

VenueOpen Forum Infectious Diseases · 2022
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineMedicaidRate ratioIncidence (geometry)QuartileOsteomyelitisConfidence intervalInternal medicineSurgeryHealth care

Abstract

fetched live from OpenAlex

Abstract Background Changes in management of bone infections, particularly early transition to oral antibiotic therapy, have decreased length of stay (LOS) for children hospitalized with acute osteomyelitis. However, evaluation of differences in length of stay by race and ethnicity have been limited. Methods Using the Kids’ Inpatient Database, we conducted a cross-sectional study of children ages 0–20 years hospitalized in 2016 or 2019 with a primary or secondary diagnosis of acute osteomyelitis. Using survey-weighted negative binomial regression, we modeled length of study (LOS) by race and ethnicity, accounting for clinical and hospital characteristics and socioeconomic status. Secondary outcomes included predictors of LOS > 7 days (equivalent to LOS in the highest quartile), central venous catheter (CVC) placement, and time to debridement. Results We identified 2,388 patients discharged with acute osteomyelitis. Median LOS was 5 days (IQR 3–7). Black race (adjusted incidence rate ratio [aIRR] 1.15, 95%CI 1.05–1.27), Hispanic ethnicity (aIRR 1.11, 95% CI 1.02–1.21), and other race and ethnicity (aIRR 1.12, 95% CI 1.01–1.23) were associated with longer LOS, compared to White race (Figure 1). Additional factors associated with prolonged LOS were Medicaid/self-pay status (aIRR 1.14, 95% CI 1.07–1.21) and other insurance (aIRR 1.21, 95% CI 1.02–1.45), compared to private insurance; debridement procedure (aIRR 1.31, 95%CI 1.23–1.31); CVC placement (aIRR 1.41, 95% CI 1.31–1.51), and complex chronic condition (aIRR 1.21, 95% CI 1.11–1.33). The odds of Black children experiencing LOS > 7 days was 46% higher compared to White children (aOR 1.46, 95% CI 1.01–2.11; Figure 2). There were no differences by race and ethnicity on odds of CVC placement or time to debridement. Model is adjusted for hospital location/teaching status, hospital region, year, debridement procedure, CVC placement, complex chronic condition, weekend admission, discharge quarter, hospital bed size, Zip code median income quartile. Model is adjusted for hospital location/teaching status, hospital region, year, debridement procedure, CVC placement, complex chronic condition, weekend admission, discharge quarter, hospital bed size, Zip code median income quartile. Conclusion Black, Hispanic, and other race and ethnicity children with acute osteomyelitis experienced longer LOS than White children. Further research into mechanisms underlying these differences, including social determinants such as access to care or structural racism, may be important to improve care for children with osteomyelitis. Disclosures All Authors: No reported disclosures.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.010
GPT teacher head0.272
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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