General Practitioners’ Perceptions of Their Ability to Identify and Refer Patients with Suspected Axial Spondyloarthritis: A Qualitative Study
Bibliographic record
Abstract
OBJECTIVE: To explore the knowledge, beliefs, and experiences of general practitioners (GP) about inflammatory back pain (IBP) and axial spondyloarthritis (axSpA) and potential barriers for referral of patients suspected of having axSpA. METHODS: A qualitative study involving semistructured interviews with GP was conducted. Transcripts of the interviews were independently read and annotated by 2 readers. Illustrative themes were identified and a coding system to categorize the data was developed. RESULTS: Ten GP (all men; mean age 49 yrs) were interviewed. All could adequately describe "classic" ankylosing spondylitis (AS) and mentioned chronic back pain and/or stiffness as key features. All GP thought that AS is almost exclusively diagnosed in men. Six GP knew that there is a difference between mechanical back pain and IBP, but could recall only a limited number of variables indicative of IBP, such as awakening night pain (4 GP), insidious onset of back pain (1 GP), improvement with movement (1 GP), and (morning) stiffness (2 GP). Two GP mentioned peripheral arthritis as other SpA features, none mentioned dactylitis or enthesitis. GP awareness of associated extraarticular manifestations was low. Most GP expressed that (practical) referral measures would be useful. CONCLUSION: GP are aware of "classic", but longterm features of axSpA. Knowledge about the disease spectrum and early detection is, however, limited. Addressing these issues in training programs may improve recognition of axSpA in primary care. This may ultimately contribute to earlier referral, diagnosis, and initiation of effective treatment in patients with axSpA.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".