OA13 Preliminary results from the National Axial Spondyloarthritis Society time to diagnosis audit: two years on
Notice bibliographique
Résumé
Abstract Background/Aims Time to diagnosis (TTD) is a worldwide problem in axial spondyloarthritis (axial SpA), with the UK faring worse than many other countries. Extended delays are associated with functional decline, poorer psychological well-being and higher healthcare costs. In June 2021, the National Axial Spondyloarthritis Society (NASS) launched the ‘Act on Axial SpA’ campaign in a bid to reduce the TTD to 12 months. A survey was created by NASS and UK rheumatology teams to explore the patient journey from symptom onset to diagnosis and to assess the national performance. Methods An online survey was launched in October 2022 for people newly diagnosed with axial SpA. The survey comprised 7 demographic questions and 6 questions pertaining to the patient journey to diagnosis. Hospital governance approval was sought locally and patient information leaflets, posters and QR codes were distributed in UK clinics. To reduce potential recall bias, only data from patients diagnosed between January 2021 and September 2024 were included in the analysis. Results Five hundred and fifty-three patients from 54 UK rheumatology departments were included: 46.7% female (n = 258), mean age at symptom onset 31.8 years (SD 12.67) and the mean age at diagnosis 39.9 years (SD 12.95). The mean and median total TTD were 8 years (SD 8.91) and 4.7 years (IQR 9.25), respectively. Table 1 shows the TTD data by year, stage of patient journey and gender. Conclusion Preliminary results from a UK-based audit may indicate a trend towards reduced TTD in axial SpA, seemingly due to quicker referral from primary care to rheumatology and faster assessment in rheumatology. Men experience a shorter TTD, however the improvement in TTD for women is greater between 2021 and 2024. A sustained, nationwide effort is paramount to build upon these positive signs and reduce the TTD in axial SpA in the UK. Disclosure T.A. Ingram: Other; Institutional grant funding from AbbVie, Biogen, Janssen, Lilly, Novartis and UCB. J. Eddison: Other; Institutional grant funding from AbbVie, Biogen, Janssen, Lilly, Novartis and UCB. A. Chan: Honoraria; A.C. has received speaker fees and travel support from Novartis, UCB, Lilly, AbbVie, Amgen, Medacs, and Janssen. M. Chan: Honoraria; M.C. has received honoraria and sponsorship from UCB, Novartis, AbbVie and Lilly. D. Das: None. J. Freeston: Consultancies; J.F. has received consultancy fees from Ferring. Honoraria; J.F. has received honoraria / speaking fees from UCB, Novartis, Janssen and Acindes. W.J. Gregory: Honoraria; W.G. has received honoraria for speaking and advisory board from AbbVie, Janssen, Novartis, Pfizer, Sobi and UCB. T. Gudu: None. J. Hamilton: Other; Institutional grant funding from AbbVie, Biogen, Janssen, Lilly, Novartis and UCB. C. Clark: Honoraria; Consulting/speaker fees from AbbVie, Novartis, Galapagos, Gilead and Bristol Myers Squibb. Other; Institutional grant funding from AbbVie, Biogen, Janssen, Lilly, Novartis and UCB. S. Bamford: None. H. Tahir: None. A. Moorthy: None. K. Gaffney: Shareholder/stock ownership; K.G. is a shareholder of Rheumatology Events. Honoraria; K.G. has received honoraria or consultancy fees from Novartis, AbbVie, UCB, Lilly and Pfizer. Member of speakers’ bureau; K.G. has participated in speaker’s bureau for Novartis, UCB, AbbVie and Lilly. Grants/research support; K.G. has obtained grant support from NASS, Versus Arthritis, AbbVie, Alfasigma, Pfizer, UCB, Novartis, Eli Lilly, Medacpharma, Celltrion, Janssen and Biogen. Other; K.G. has received meeting expenses from AbbVie, Lilly, Roche, Novartis, Pfizer and UCB. R. Sengupta: Honoraria; R.S. has received honoraria for speaking and attending advisory boards with Pfizer, AbbVie, Biogen, BMS, Lilly, Novartis and UCB. Grants/research support; R.S. has received grants from UCB, BMS, AbbVie and Novartis. Other; R.S. has been sponsored to attend regional, national and international meetings by UCB, AbbVie, Novartis and Lilly. D. Webb: Other; Institutional grant funding from AbbVie, Biogen, Janssen, Lilly, Novartis and UCB.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,042 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».