Diagnostic accuracy and trajectories of referrals for gout to rheumatology
Bibliographic record
Abstract
• Non-rheumatologists, especially those in acute care specialties, are accurate in diagnosing gout, suggesting care gaps stem from suboptimal treatment, rather than inaccurate diagnosis • Gout mimickers include conditions with mono/oligoarticular involvement and/or intermittent periods of disease flares • Male sex, serum urate ≥500 µmol/L, lower extremity monoarthritis and symptom duration ≤2 weeks may be useful at point of referral triage to ascertain a final gout diagnosis Objectives: To evaluate diagnostic accuracy and trajectories of gout referrals to rheumatology including factors associated with an accurate diagnosis. Methods: We performed a retrospective cohort study of referrals at 4 rheumatology clinics in Brampton, Canada from December 2019 to January 2023. We assessed gout diagnostic accuracy referenced to the rheumatologist’s “gold standard” diagnosis, describing alternative final diagnoses. Using multivariable logistic regression, we identified factors associated with an accurate gout diagnosis. Results: Among 4,315 patients, 216 were diagnosed with gout. Of 191 gout referrals (mean (SD) age 58.4 (15.4) years; 77.0% male), the diagnosis was unchanged in 159 (83.2%) patients with alternative diagnoses comprising osteoarthritis, autoimmune inflammatory arthritis and calcium pyrophosphate deposition disease. Referring physicians had moderate-to-high sensitivity (73.6%, 95% CI: 67.2–79.4), specificity (99.2%, 95% CI: 98.9–99.5), positive predictive value (83.2%, 95% CI: 77.2–88.2), negative predictive value (98.6%, 95% CI: 98.2–99.0) and inter-rater reliability (Cohen’s kappa: 0.77, 95% CI: 0.72–0.82). Accuracy was highest amongst internists and emergency room physicians. Male sex (OR 14.32, 95% CI: 4.44–46.17), serum urate ≥500 µmol/L (OR 9.10, 95% CI: 2.19–7.78), lower extremity monoarthritis (OR 5.08, 95% CI: 1.59–16.27) and symptom duration ≤2 weeks (OR 3.87, 95% CI 1.23–12.21) were predictive of a final gout diagnosis. Conclusions: Referring providers had reasonably high accuracy in diagnosing gout. Traditional risk factors were associated with concordance with the consultant rheumatologist. Suboptimal gout care likely does not stem at point-of-diagnosis and quality improvement efforts should be focused on mitigating treatment-associated care gaps.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".