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Enregistrement W4313532424 · doi:10.1136/lupus-2022-lupus21century.26

605 The Systemic Lupus Erythematosus International Collaborating Clinics (SLICC), American College of Rheumatology (ACR), and Lupus Foundation of America (LFA) Damage Index Revision – Item Generation Phase

2022· article· en· W4313532424 sur OpenAlexaffabout
Burak Kundakci, Ann E. Clarke, Sindhu R. Johnson, Hermine I. Brunner, Jiacai Cho, N. Costedoat‐Chalumeau, Ellen M. Ginzler, John G. Hanly, Abida Hasan, Murat İnanç, Naureen Kabani, Kaitlin Lima, Lívia Lindoso, Anselm Mak, Rosalind Ramsey- Goldman, Guillermo Ruiz‐Irastorza, Clóvis A. Silva, Farah Tamirou, Vitor Trindade, Évelyne Vinet, Ian N Bruce, Megan R.W. Barber

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineMedicine
ThématiqueSystemic Lupus Erythematosus Research
Établissements canadiensQueen Elizabeth II Health Sciences CentreUniversity of TorontoDalhousie UniversityToronto Western HospitalMount Sinai HospitalMcGill UniversityUniversity of Calgary
Organismes subventionnairesNational Institutes of Health
Mots-clésSystemic lupus erythematosusMedicineConstruct (python library)DelphiDelphi methodConstruct validityInternal medicineComputer scienceClinical psychologyArtificial intelligenceDiseasePsychometrics

Résumé

récupéré en direct d'OpenAlex

Background The SLICC, ACR and LFA embarked on a data- and expert-driven project to develop a revised systemic lupus erythematosus (SLE) organ damage index (SDI). The methodological approach includes 5 phases: updating the construct of damage (I), item generation (II), item reduction (III), item weighting and threshold determination (IV), and the assessment of validation and reliability (V). In phase I, a consensus statement was developed to define the construct of damage in SLE1. In the Item Generation phase, we aimed to develop and agree on a candidate list of items that reflect the construct of damage in SLE and are appropriate to be included in a new damage index including consideration of relevant items from adult, paediatric and young adult SLE. In this analysis, we compare the two approaches to initial item generation that were employed in a parallel process, namely a literature review and a Delphi exercise. Methods Item generation included a literature review and 3-part Delphi exercise. A group of lupus experts conducted a literature review to identify items that reflect the construct of damage in SLE and grouped the items into organ domains. Each domain was reviewed by paediatric rheumatologists. Snow-ball sampling was used among SLICC members, asking them to nominate 3-4 SLE experts considering a range of clinical expertise, equality, diversity and inclusiveness factors, and the global nature of SLE research. The LFA, Lupus UK, Lupus Europe and Lupus Canada were also asked to nominate 4-6 patient/carer representatives to participate in the Delphi exercise. Participants were asked to nominate items that should be included in a revised damage index based on the updated construct definition1 using a free-text option in Delphi exercise. Results We established a group of 146 individuals (mean age 50.6 ranging from 28 to 79 years; 60.3% females; 58.9% white; clinical experience from 1 to 51 years) from 35 countries, broadly representative of the lupus research and patient community. There were 135 medical doctors, 2 allied health professionals and 9 patients. Of 135 medical doctors, 120 were rheumatologists, 7 internists, 5 nephrologists, 2 dermatologists, and 1 immunologist. The response rate after the first round Delphi exercise was 97.9%. All items in the original SDI were nominated in both processes. Item generation yielded approximately 2,600 items. After rationalising for repetition, redundancy, and harmonisation of synonyms, 220 unique items were identified across 14 organ systems. The literature review proposed 4 (1.8%) unique items, 103 (46.8%) unique items were from the Delphi only and 113 (51.4%) items appeared in both exercises (figure 1). Conclusion Using a combined data-driven and expert/patient-based approach, items and domains that comprise damage in SLE have been expanded. Just over half of all items were nominated by both approaches. However, the Delphi exercise which included a wide and diverse group of contributors, provided a large number of unique items for further consideration. Our data confirms the value of large group exercises early in such a process to maximise the scope of new items to consider for a revised index. Reference Johnson, S. R et al. Evaluating the construct of damage in SLE. Arthritis Care Res. 2021. Lay Summary The SLICC/ACR Damage Index (SDI) (published in 1996) is widely used in clinical studies and trials to measure the long-term complications that can occur in lupus patients, such as cataracts, fractures, and kidney failure. Higher scores are associated with poorer quality of life, as reported by patients. A number of drawbacks have also been found with the SDI. We need to better understand and measure the impact of these complications from a patient and doctor’s perspective to get a much deeper understanding of how SLE affects people. We used two methods to generate new items to include in an updated SDI. First, we used the medical literature to identify possible complications of lupus. Then, we asked a large group of lupus experts and patients to nominate complications. The process generated approximately 2,600 items. After removing redundant suggestions, 220 unique items were identified. The literature review proposed 4 (1.8%) unique items, 103 (46.8%) unique items were from the large group only and 113 new (51.4%) items appeared in both exercises. Our data shows the value of large group exercises that include patient representatives, to maximise the scope of new items to consider for a revised index.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,048
score de la tête « metaresearch » (Gemma)0,071
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Méthodes · Signal consensuel: aucune
Score de désaccord entre enseignants0,048
Score d'incertitude au seuil0,255

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0480,071
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0040,004
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0010,003
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0330,006

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,025
Tête enseignante GPT0,341
Écart entre enseignants0,315 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission2
Résumé présentoui

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