P085 NRAS New2RA Right Start for people newly/recently diagnosed with RA
Bibliographic record
Abstract
Abstract Background/Aims The National Rheumatoid Arthritis Society (NRAS) follows best practice, evidence-based standards in all we do. Whilst huge strides have been made in the diagnosis and treatment of Rheumatoid Arthritis (RA), the impact on quality of life can be significant and for many, RA remains hard to come to terms with. Anxiety and depression are frequent co-morbidities seen in RA, particularly in the early stages. This can impede people’s ability to acquire knowledge about their disease together with supported self-management skills and coping strategies. The aim of Right Start is to improve outcomes of the newly/recently diagnosed with RA through a framework of emotional, educational and peer support, and access to high quality supported self-management resources tailored to individual need. Methods Right Start involves a simple, 4-step process: • a call (up to 1 hr) with a member of our helpline. • 1:1 peer support from trained volunteers with RA, 24/7 online community support • a tailored package of hard copy information with e-links of interest sent by post • further follow up available by helpline and individual peer support Right Start enables health professionals to meet their responsibilities against NICE Quality Standard 33, Statement 3, on which they are audited through National Early Inflammatory Arthritis Audit (NEIAA), and the EULAR Recommendations for implementation of self-management strategies in inflammatory arthritis. Results Since launch at BSR 2019, 71 rheumatology units have referred over 400 patients to this service. Approx. 1/3rd couldn't be contacted and after 3 attempts, a letter and newly diagnosed pack are sent, inviting contact at future date. Anecdotally people are highly satisfied with this service and a number of units are referring multiple patients. To gather empirical data on the impact of the service NRAS has partnered with Manchester University to undertake an Enhanced Right Start pilot in 5 UK rheumatology units involving the use of validated patient reported outcome and experience measures from the users recruited, and quantitative and qualitative data from the health professionals. The pilot is due to commence before end 2021. Meantime, referrals continue to grow. Conclusion The NHS rheumatology workforce is in crisis and Right Start enables professionals to offer evidence-based education and supported self-management resources to optimise patient care. A Nurse Consultant (West Middx.) said of the service: “I have found Right Start to be an excellent resource for people who have recently been diagnosed with RA. When time is limited time in clinic, a referral provides the patient with tailored information which complements the education I have been able to provide. Feedback from patients has been positive particularly relating to the phone calls with the NRAS helpline team and the NRAS volunteer with RA. I would definitely recommend the service.” Disclosure A.M. Bosworth: None. I. McNicol: None.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.083 | 0.012 |
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".