Disparities in Time to Diagnosis of Radiographic Axial Spondyloarthritis
Bibliographic record
Abstract
OBJECTIVE: Radiographic axial spondyloarthritis (r-axSpA) has a 7-year average diagnostic delay. Although the effects of sex or gender on time to diagnosis have been evaluated, the role of social determinants of health remains understudied. We assessed whether time from initial clinical documentation of r-axSpA symptoms to r-axSpA diagnosis (diagnostic delay) varies based on sex, race, ethnicity, and/or the presence of social needs. METHODS: We studied patients with r-axSpA from a tertiary center from 2000 to 2022. The cohort was built with the Observational Health Data Sciences and Informatics (OHDSI) network. For the primary analysis, we assessed the time from back pain and/or spinal pain to r-axSpA diagnosis and, secondarily, the time to r-axSpA from any other r-axSpA-related condition. To estimate differences in diagnostic delay, we employed an accelerated failure time parametric survival model. RESULTS: We included 404 patients (mean age 49 years; 38.6% female), with 25.5% identifying as Black, 31.1% as other or unknown race, and 14.1% as Hispanic. Patients with a documented social need had a 21% increase in time from back pain to r-axSpA diagnosis (95% CI 0.93-1.56). In patients with any r-axSpA-related condition, time to diagnosis similarly increased by 21% (95% CI 0.92-1.57). Considering that there is an average time to diagnosis of 34 months, a social need increased time to diagnosis by 7 months. CONCLUSION: This study reveals a trend toward diagnostic delay in r-axSpA related to social need, sex, race, and ethnicity. Future studies should focus on referral strategies to enable prompt diagnosis and optimize care.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".