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Record W2795130162 · doi:10.1136/lupus-2018-abstract.5

S1D:5 Sle disease activity index glucocorticosteroid index (sledai-2kg) identifies more responders than sledai-2k

2018· article· en· W2795130162 on OpenAlexaff
Murray B. Urowitz, Gladman Dd, Jiandong Su, N. Anderson, Zahi Touma

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicinePrednisoneSystemic lupus erythematosusInternal medicineClinical trialDiseasePhysical therapy

Abstract

fetched live from OpenAlex

Background/purpose Systemic Lupus Erythematosus Disease Activity Index-2000 (SLEDAI-2K) is one of the most commonly used disease activity indices in clinical practice and research but this index doesn’t account for severity within each descriptor. Moreover, in clinical trials, the use of standard of care (SoC), which includes glucocorticosteroid (GCS) often confounds trial results. We developed and validated a novel lupus disease activity index, SLEDAI-2K GCS (SLEDAI-2KG), that describes disease activity while accounting for GCS dose. SLEDAI-2KG has the same descriptors as SLEDAI-2K in addition to a new descriptor ‘GCS’ with different weight scores based on the dose of GCS. Furthermore, SLEDAI-2KG has a low administration burden and a simple scoring system similar to SLEDAI-2K. We aimed to compare the performance of SLEDAI-2K and SGI in identifying responders in response to SoC. Methods Patients have been followed prospectively according to a standard protocol between January 2011 and January 2014, at a single lupus centre, with active disease (SLEDAI-2K≥6), on prednisone ≥10 mg/day, and with follow up visits within 5–24 months were studied. Treatment was determined based on the judgment of the treating rheumatologist. Response to SoC therapy, at first follow up visit, was assessed by SLEDAI- 2K and SLEDAI-2KG. Responders were defined based on the decrease in SLEDAI-2K and SGI score by ≥4. The performance of SLEDAI-2K and SGI was also compared using different cut-off points; 5, 6 and 7. Descriptive analysis was used. Results 111 patients met the inclusion criteria of the study and were further analysed. Patients’ characteristics are represented in table 1. SLEDAI-2KG identified more responders at 6 months (94% vs 84%) and at 12 months (92% vs 76%) compared to SLEDAI-2K by cut off of 4. SLEDAI-2KG also identified more responders with cut off points 5, 6 and 7 (table 2). Conclusion The novel index, SLEDAI-2KG, is superior to SLEDAI-2K in identifying responders at 6 and 12 months accounting for steroid dose and thus adjusting for severity within each descriptor of SLEDAI-2K. SLEDAI-2KG has the ability to enhance analyses in clinical trials to differentiate between responders on minimal and moderate/large doses of GCS.

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 score0.032
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0320.004

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 head0.028
GPT teacher head0.334
Teacher spread0.306 · 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".

Quick stats

Citations3
Published2018
Admission routes1
Has abstractyes

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