Republished: A systematic literature review of strategies promoting early referral and reducing delays in the diagnosis and management of inflammatory arthritis
Bibliographic record
Abstract
BACKGROUND: Despite the importance of timely management of patients with inflammatory arthritis (IA), delays exist in its diagnosis and treatment. OBJECTIVE: To perform a systematic literature review to identify strategies addressing these delays to inform an American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) taskforce. METHODS: The authors searched literature published between January 1985 and November 2010, and ACR and EULAR abstracts between 2007-2010. Additional information was obtained through a grey literature search, a survey conducted through ACR and EULAR, and a hand search of the literature. RESULTS: (1) From symptom onset to primary care, community case-finding strategies, including the use of a questionnaire and autoantibody testing, have been designed to identify patients with early IA. Several websites provided information on IA but were of varying quality and insufficient to aid early referral. (2) At a primary care level, education programmes and patient self-administered questionnaires identified patients with potential IA for referral to rheumatology. Many guidelines emphasised the need for early referral with one providing specific referral criteria. (3) Once referred, early arthritis clinics provided a point of early access for rheumatology assessment. Triage systems, including triage clinics, helped prioritise clinic appointments for patients with IA. Use of referral forms standardised information required, further optimising the triage process. Wait times for patients with acute IA were also reduced with development of rapid access systems. CONCLUSIONS: This review identified three main areas of delay to care for patients with IA and potential solutions for each. A co-ordinated effort will be required by the rheumatology and primary care community to address these effectively.
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 | 0.016 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".