Characterization of Patients with Arthritis Referred for Gold Therapy in the Era of Biologics
Bibliographic record
Abstract
OBJECTIVE: To describe the clinical characteristics of patients referred for gold therapy and determine the reason for referral. METHODS: We conducted a chart review of patients referred for gold at the Mary Pack Arthritis Program, Vancouver, Canada, from July 2007 to July 2009. RESULTS: The sample included 69 female and 12 male patients. Diagnosis was rheumatoid arthritis (RA) in 71/81, psoriatic arthritis in 5, juvenile idiopathic arthritis (JIA) in 2, Sjögren syndrome in 1, undifferentiated polyarthritis in 1, and spondyloarthritis in 1. Twenty of 81 patients had received gold before: 15 were referred for a second course, 4 a third course, and 1 a fourth course. Ten of 81 patients were referred for gold as their first disease-modifying antirheumatic drug (DMARD). Seventy-one had received prior DMARD: 1 prior DMARD in 22 patients, 2 in 24 patients, 3 in 15 patients, and > 3 in 6 patients. Four patients had received prior biologic therapy plus 2 to 4 prior DMARD. Twelve of 71 received gold monotherapy, 56/71 received gold/DMARD combinations, and 3 received gold/biologic/DMARD combinations. Reasons for referral included failure of other DMARD in 54 patients, limited DMARD options in 50 (chronic liver disease in 34, sulfa allergy in 7, high alcohol consumption in 5, and planning pregnancy in 4), physician choice in 12, previous benefit from gold in 10, benefit of clinic support in 10, inappropriate for biologics in 7, patient choice in 4, and failure of biologics in 3. CONCLUSION: The most common reasons for referral to gold clinic in 2007 to 2009 are failure of other DMARD and limited DMARD options due to underlying liver disease.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".