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Record W1991864911 · doi:10.1177/0961203313492872

Damage in lupus patients—what have we learned so far?

2013· article· en· W1991864911 on OpenAlexaff
Lihi Eder, Murray B. Urowitz, DD Gladman

Bibliographic record

VenueLupus · 2013
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineRheumatologyDiseaseSystemic lupus erythematosusDamage controlInternal medicineSystemic lupusEnd organ damageIntensive care medicineSurgery

Abstract

fetched live from OpenAlex

With the improvement of survival of systemic lupus erythematosus (SLE) patients, prevention of organ damage has become a major goal in the management of these patients. The need for a reliable tool for assessment of cumulative damage over time led to the development of the Systemic Lupus International Collaborating Clinics/American College of Rheumatology (SLICC/ACR) damage index (SDI) that allowed a standardized measurement of damage and facilitated research in the field. Damage accumulates over the course of the disease; however, some patients do not accrue any damage even after many years. Damage accrues at a higher rate among patients with more active disease, highlighting the importance of prompt control of disease manifestations. However, medications administered to control disease activity, particularly corticosteroids, can also result in organ damage, emphasizing the need for adjustment of these agents as soon as disease is under control.

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)0.008
metaresearch head score (Gemma)0.031
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0020.002
Science and technology studies0.0010.004
Scholarly communication0.0060.011
Open science0.0030.002
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0040.002

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 head0.027
GPT teacher head0.298
Teacher spread0.271 · 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
GenreReview

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

Citations15
Published2013
Admission routes1
Has abstractyes

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