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Record W2520518289 · doi:10.1136/lupus-2016-000179.105

CE-26 From childhood to adulthood: identifying latent classes of disease activity trajectories in childhood-onset systemic lupus erythematosus patients

2016· article· en· W2520518289 on OpenAlexaffabout
Lily SH Lim, Eleanor Pullenayegum, Lillian Lim, Brian M. Feldman, Dafna D. Gladman, Earl D. Silverman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsInstitute for Clinical Evaluative SciencesToronto Western HospitalUniversity of ManitobaUniversity of TorontoSickKids FoundationChildren's Hospital Research Institute of Manitoba
Fundersnot available
KeywordsMedicinePrednisoneSystemic lupus erythematosusCohortLongitudinal studyDiseasePopulationLatent class modelPediatricsCohort studyInternal medicinePathology

Abstract

fetched live from OpenAlex

Background Although SLE patients are thought to follow different patterns of disease courses, no information is available about the longitudinal disease activity or the number of possible different disease courses. This study sought to: 1) Assess for distinguishable differences in disease activity trajectories in childhood-onset SLE (cSLE) patients; 2) Identify factors predictive of membership in different classes and 3) Assess if different disease activity trajectories are associated with different damage trajectories. Methods Single-centre longitudinal inception cohort of cSLE patients (onset < 18 years) diagnosed and followed from Jan 1985 to Sep 2011. Paediatric data was obtained from our institutional cSLE database and adult data from the Toronto Lupus database or from rheumatologists’ offices. Longitudinal disease trajectory was constructed using data from every clinic visit in the 1st 10 years after diagnosis. Longitudinal SLE activity is a latent construct that is imperfectly measured with SLE disease activity index 2000 (SLEDAI2K) and prednisone exposure. SLEDAI2K and prednisone use were then jointly modelled in a Bayesian growth mixture model (GMM). Baseline factors were tested for prediction of class membership. Results 473 patients were included. 82% were female, median age of diagnosis was 14.1 years. There were 11992 visits, 2666 patient years. 67% of the population had transferred to adult care. Mean population SLEDAI2K and prednisone trajectories of cSLE patients showed rapid decline to low activity levels within 2 years after diagnosis. Joint GMM showed 5 latent classes in this cohort of cSLE patients. Class 1 patients (6%) have chronic moderate-high disease activity, class 2 (12%) had moderate initial disease activity and continued moderate long-term prednisone use, class 3 (17%) had initial high disease activity but achieved long-term remission, class 4 (19%) had high initial disease activity but relapsed later, class 5 (45%) had chronic low-grade disease activity. Across all classes, there was chronic use of prednisone (at least 5–10 mg/day) among cSLE patients in the first 10 years after diagnosis. Baseline major organ involvement, ethnicity, age at diagnosis and the number of baseline ACR criteria predicted probability of membership in different classes. Class 1 was associated with the most average damage accrual while class 5 was not associated with significant average damage accrual even after 10 years. Conclusions cSLE patients could be sub-classified into 5 distinct classes of disease activity trajectories. Baseline and demographic factors predicted membership in the distinct disease classes. Different disease classes were associated with different patterns of damage trajectories.

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)0.004
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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.280
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
Published2016
Admission routes2
Has abstractyes

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