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Record W2041986341 · doi:10.1186/ar3992

Variation in renal biopsy and medication prescribing practices among pediatric Medicaid patients with lupus nephritis prior to end-stage renal disease in the US, 2000 to 2004

2012· article· en· W2041986341 on OpenAlexfundno aff
LT Hiraki, CH Feldman, J Liu, Alarcón Gs, MA Fischer, WC Winkelmayer, KH Costenbader

Bibliographic record

VenueArthritis Research & Therapy · 2012
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
FundersNational Institutes of HealthCanadian Arthritis NetworkNational Institute of Arthritis and Musculoskeletal and Skin DiseasesArthritis SocietyLupus Research AllianceArthritis Foundation
KeywordsMedicineLupus nephritisMedicaidRheumatologyEnd stage renal diseaseRenal biopsyInternal medicineDiseaseStage (stratigraphy)BiopsyNephrologyIntensive care medicine

Abstract

fetched live from OpenAlex

Unequal medical care may contribute to striking sociodemographic disparities seen in outcomes for children with lupus nephritis. Medicaid is the US federal-state program providing health insurance to low-income children and parents. We investigated US nationwide variation in renal biopsies and medication prescriptions for children with lupus nephritis enrolled in Medicaid, 2000 to 2004, in the months preceding end-stage renal disease (ESRD). We identified all children aged 3 to <18 years with SLE (≥3 ICD-9 codes of 710.0, each >30 days apart) in the Medicaid Analytic eXtract (MAX) from 2000 to 2004, which contains outpatient and inpatient Medicaid claims for enrollees in 47 US states and the District of Columbia. These data were linked to the US Renal Data System, with information on essentially all ESRD patients in the US, for the same years. We compared frequencies of renal biopsies, and prescription of corticosteroids, hydroxychloroquine (HCQ) and immunosuppressants (mycophenylate mofetil (MMF), cyclophosphamide (CYC), cyclosporine, azathioprine (AZA), tacrolimus), across categories of sex, race/ethnicity, socioeconomic status (SES), US region of residence, residence in a designated Health Professional Shortage Area (HPSA), and quartiles of pediatric rheumatologist number in state of residence. We tested for differences across categories using chi-squared and Fisher's exact tests, and applied the Cochrane Armitage test for trend. Of the 254 pediatric lupus nephritis patients who developed ESRD, the mean age was 14.2 (± 2.4) years; 72% were female, 61% were African American and 19% were Hispanic. The mean time from first SLE claim to ESRD was 3.8 (± 2.1) years. A total of 46% had at least one renal biopsy preceding ESRD. More children in the lower quartiles of SES and the higher quartiles of rheumatologist number per state, received a biopsy ( P trend < 0.05) (Table 1 ). Ninety-one percent of children were prescribed steroids at some time preceding ESRD, 63% were prescribed HCQ and 66% any other immunosuppressant, 50% of whom were prescribed MMF, 30% AZA and 14% CYC. We observed variation in prescribed steroids across region of residence, HCQ and immunosuppressant across race (more non-White patients prescribed both medications), and a greater proportion of patients prescribed HCQ in states with a higher number of rheumatologists per state. We observed significant differences in the proportion of children who had received renal biopsies across categories of SES and rheumatologist number per state, as well as marked differences in medication prescribing across categories race, SES, regions of residence and rheumatologist number in state of residence.

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)0.003
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 score0.101
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.035
GPT teacher head0.335
Teacher spread0.300 · 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
Published2012
Admission routes1
Has abstractyes

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