OA13 Preliminary results from the National Axial Spondyloarthritis Society time to diagnosis audit: two years on
Bibliographic record
Abstract
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.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".