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Record W2902004501

Predicting Mortality in Patients with Rheumatoid Arthritis Related Interstitial Lung Disease: Expanding The GAP Model

2016· article· en· W2902004501 on OpenAlexaff
Julie Morisset, Eric Vittinghoff, Bo Young Lee, Tonelli Tonelli, Xiaowen Hu, Brett M. Elicker, Jay H. Ryu, Kirk D. Jones, Stefania Cerri, Andreina Manfredi, Marco Sebastiani, Brett Ley, Paul J. Wolters, Talmadge E. King, Dong Soon Kim, Harold R. Collard, Joyce Sujin Lee

Bibliographic record

VenueIris Unimore (University of Modena and Reggio Emilia) · 2016
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsRheumatoid arthritisInterstitial lung diseaseMedicineDiseaseLungIntensive care medicineInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Rationale: Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is a disease associated with morbidity and mortality. There is a high prevalence of usual interstitial pneumonia (UIP) pattern in RA-ILD and similarities have been observed between patients with idiopathic pulmonary fibrosis (IPF) and the UIP form of RA-ILD. The GAP (gender, age, physiology) model has been shown to predict mortality in patients with IPF, but its ability to predict mortality in RA-ILD is not known. Methods: We identified 309 patients with RA-ILD at 4 academic centers with ongoing longitudinal cohorts of patients with ILD (Mayo Clinic, University of Ulsan, University of California, San Francisco and University of Modena & Reggio Emilia). The primary endpoint was mortality. To handle the issue of missing data, multiple imputation by iterative chained equations was used, resulting in 20 completed datasets. Linear and logistic imputation models were used for continuous and binary covariates, respectively. Using the GAP model as the baseline mortality prediction model, we determined the additive model performance for mortality risk prediction by incorporating additional variables. Model discrimination was assessed using the c-index. Results: Patients had a mean age of 65 years and were predominantly female (54%). The mean forced vital capacity % predicted was 73 and the mean diffusing capacity for carbon monoxide (DLCO) % predicted was 55. The majority of patients had positive rheumatoid factor (RF) (89%) or positive anti-cyclic citrullinated peptide antibody (71%). Twenty-four percent of patients had a definite UIP pattern on high-resolution computed tomography (HRCT). The original GAP model (Gender, Age, FVC%, DLCO%) had a c-index of 0.69 in our cohort. The performance of this model improved by expanding the GAP model to include 2 additional variables: definite UIP pattern on HRCT and positive RF. The c-index of this expanded model was 0.71. Conclusions: The addition of 2 variables, definite UIP pattern on HRCT and RF, improves the performance of the GAP model to predict mortality in patients with RA-ILD.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.119
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.010
GPT teacher head0.212
Teacher spread0.203 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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