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Systemic lupus erythematosus

2023· article· en· W4382239389 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Nurse Practitioner · 2023
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsMount Royal University
Fundersnot available
KeywordsMedicineDiscontinuationHydroxychloroquineIntensive care medicineAdverse effectClinical trialCyclophosphamideDiseaseDrugLupus erythematosusSystemic lupus erythematosusPharmacotherapyInternal medicineImmunologyPharmacologyChemotherapyCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

ABSTRACT: Drug therapy for patients with systemic lupus erythematosus (SLE) aims to decrease symptom severity. Pharmacologic interventions are divided into four categories: antimalarials, glucocorticoids (GCs), immunosuppressants (ISs), and biological agents. Hydroxychloroquine, the most commonly used antimalarial treatment for this disease, is a mainstay in treating all patients with SLE. The multitude of adverse reactions of GCs has led clinicians to minimize their dosages or discontinue them whenever possible. To speed up the discontinuation or minimization of GCs, ISs are used for their steroid-sparing properties. Furthermore, certain ISs such as cyclophosphamide are recommended as maintenance agents to prevent flares and reduce the reoccurrence and severity of the disease state. Biological agents are recommended when other treatment options have failed due to intolerance or inefficacy. This article presents pharmacologic approaches for managing SLE in patients based on clinical practice guidelines and data from randomized controlled trials.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.778
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.016

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 head0.034
GPT teacher head0.329
Teacher spread0.296 · 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