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Record W2902174662 · doi:10.1136/rmdopen-2018-000793

Systemic lupus erythematosus: state of the art on clinical practice guidelines

2018· review· en· W2902174662 on OpenAlexaff
Farah Tamirou, Laurent Arnaud, Rosaria Talarico, Carlo Alberto Scirè, Tobias Alexander, Zahir Amoura, Tadej Avčin, Alessandra Bortoluzzi, Ricard Cervera, Fabrizio Conti, Alain Cornet, H. Devilliers, Andrea Doria, Micol Frassi, Micaela Fredi, Marcello Govoni, Frédéric Houssiau, Ana Lladó, Carla Macieira, Thierry Martin, Laura Massaro, Maria Francisca Moraes‐Fontes, Cristina Pamfil, Sabrina Paolino, Chiara Tani, Sander W. Tas, Maria G. Tektonidou, Anǵela Tincani, Ronald van Vollenhoven, Stefano Bombardieri, Gerd R Burmester, João Eurico Fonseca, Ilaria Galetti, É. Hachulla, Ulf Müeller-Ladner, Matthias Schneider, Vanessa Smith, Maurizio Cutolo, Marta Mosca, N. Costedoat‐Chalumeau

Bibliographic record

VenueRMD Open · 2018
Typereview
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsInstitute of Infection and Immunity
FundersEuropean Commission
KeywordsMedicineIntensive care medicineQuality of life (healthcare)DiseaseClinical PracticeLupus nephritisDisease managementPhysical therapyInternal medicineNursing

Abstract

fetched live from OpenAlex

Systemic lupus erythematosus (SLE) is the paradigm of systemic autoimmune diseases characterised by a wide spectrum of clinical manifestations with an unpredictable relapsing-remitting course. The aim of the present work was to identify current available clinical practice guidelines (CPGs) for SLE, to provide their review and to identify physicians' and patients' unmet needs. Twenty-three original guidelines published between 2004 and 2017 were identified. Many aspects of disease management are covered, including global disease management, lupus nephritis and neuropsychiatric involvement, management of pregnancies, vaccinations and comorbidities monitoring. Unmet needs relate with disease management of some clinical manifestations and adherence to treatment. Many patient's unmet needs have been identified starting with faster diagnosis, need for more therapeutic options, guidelines on lifestyle issues, attention to quality of life and adequate education.

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)0.005
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.263
GPT teacher head0.527
Teacher spread0.264 · 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 designNot applicable
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

Citations104
Published2018
Admission routes1
Has abstractyes

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