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Record W1918221619

Summarizing disease features over time: I. Adjusted mean SLEDAI derivation and application to an index of disease activity in lupus.

2003· article· en· W1918221619 on OpenAlexaffabout
Dominique Ibañez, Murray B. Urowitz, Dafna D. Gladman

Bibliographic record

VenuePubMed · 2003
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsMedicineConfidence intervalSystemic lupus erythematosusOdds ratioInternal medicineDiseaseLupus erythematosusImmunology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To develop a measurement of lupus disease activity over time. METHODS: We studied patients from the University of Toronto Lupus Clinic with "regular" followup, defined as having been in the clinic for at least 3 visits and never having been away from the clinic for a period exceeding 18 consecutive months. For each visit, disease activity was evaluated with the Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K). The common approach to summarize over multiple visits by calculating a mean ignores the presence of varying time intervals between visits. We used the adjusted mean SLEDAI-2K (AMS), determined by the calculation of the area under the curve of SLEDAI-2K over time by adding the area of each of the blocks of visit interval and then dividing by the length of time for the whole period. The resulting AMS has the same units as the original SLEDAI-2K. A time-dependent covariate survival analysis was done to test which of AMS, SLEDAI-2K at presentation, sex, and age at diagnosis is the best predictor of mortality. RESULTS: A total of 575 patients with regular followup were included. Only AMS and age at diagnosis were significant predictors. Odds ratio (OR) and 95% confidence intervals (CI) for AMS: OR 1.15 (CI 1.09, 1.20), p = 0.0001; age at diagnosis: OR 1.05 (CI 1.03, 1.06), p = 0.0001. CONCLUSION: AMS represents an average disease activity measure over time and is strongly associated with mortality.

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)0.001
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 score0.032
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.018
GPT teacher head0.272
Teacher spread0.254 · 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

Citations161
Published2003
Admission routes2
Has abstractyes

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