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Record W1973108068 · doi:10.3899/jrheum.111106

Epidemiology of Systemic Sclerosis in a Large US Managed Care Population

2012· article· en· W1973108068 on OpenAlexvenueno aff
Daniel E. Fürst, Ancilla W. Fernandes, Şerban R. Iorga, Warren Greth, Tim Bancroft

Bibliographic record

VenueThe Journal of Rheumatology · 2012
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIncidence (geometry)EpidemiologyPopulationDiagnosis codeDemographyPediatricsInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: To estimate the incidence and prevalence of systemic sclerosis (SSc) in a large US managed care organization (MCO) database. METHODS: Subjects with claims-based evidence of SSc (ICD-9-CM code 710.1x) were identified from a health plan database. Incidence and prevalence for the period 2003-2008 were calculated. RESULTS: The overall age- and sex-adjusted incidence rate (2003-2008) for SSc was 5.6 cases per 100,000 person-years. The annual prevalence of SSc ranged from 13.5 in 2003 to 18.4 (per 100,000) in 2008. CONCLUSION: This analysis suggests a higher incidence and lower prevalence of SSc in this MCO than those previously reported for the United States.

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)0.002
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 score0.079
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.037
GPT teacher head0.292
Teacher spread0.255 · 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

Citations55
Published2012
Admission routes1
Has abstractyes

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