Referral Patterns for Dual-Energy Computed Tomography in Diagnosis and Management of Gout: Ten-Year Experience at a Canadian Institution
Bibliographic record
Abstract
PURPOSE: To analyze the utilization, indications, and outcomes of dual-energy computed tomography (DECT) gout imaging in clinical practice. METHODS: This retrospective study was ethics approved. Radiology reports of DECT gout scans between 2007 and 2016 were analyzed for trends of utilization, referral pattern, indication, and diagnosis. RESULTS: DECT gout referrals increased substantially (2007: 37; 2008: 72; 2016: 385; total: 1877). The largest number of referrals were from rheumatology (1160), emergency medicine (283), and family medicine (177). Most referrals (92%) were requested to aid an initial diagnosis of gout. Other reasons included estimating the disease burden (6%) or monitoring disease progression and effectiveness of treatment (2%). Rheumatology accounted for most referrals for the latter two reasons (81% and 97%). Imaging findings of urate presence were similar in referrals from rheumatology (62%), family medicine (62%), and other medical specialties (62%). The urate positive rates were slightly lower in referrals from emergency medicine (47%) and surgical specialties (41%). The most common differential diagnoses by referring specialties were calcium pyrophosphate dihydrate crystal deposition disease (CPPD) and other inflammatory or erosive arthritides (rheumatology, family medicine), CPPD and infections (other medical specialties), infections and fractures (emergency medicine), neoplasm and infections (surgical specialties). CONCLUSIONS: The increasing utilization of DECT for gout imaging validates its clinical value. Varying clinical presentation could explain differences of urate positive rates among specialties. Our results support a multispecialty collaborative approach to the diagnosis and management of gout, with direct access to DECT gout imaging provided to various physician specialties.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".