Bibliographic record
Abstract
Severe tophaceous gout is associated with renal impairment, alcohol, obesity, diet, hypertension, family history, and low socioeconomic status 1,2 .Treatment failure is estimated in about 1% to 1.5% of cases of gout in the United States 3 .Our patient was a 60-year-old male with gouty arthritis for 15 years and chronic renal failure for 6 years.He described irregular usage of colchicine and urate-lowering drug for the last 15 years due to noncompliance with therapy.He used neither diuretics nor other prohyperuricemic drugs.Family history was nonsignificant for gout.On physical examination, he had massive and deforming tophi and active synovitis of bilateral small joints of the hands, wrists, elbows, knees, ankles, and ears (Figure 1).There were ulcerated lesions over the tophi in both ankles (Figure 2).Laboratory data were as follows: uric acid, 8.1 mg/dl; C-reactive protein (CRP): 7.5 mg/dl; creatinine, 1.4 mg/dl; radiographs of hands revealed erosions (Figure 3).Cultures from ulcerated lesions yielded no growth.The patient was confined to a wheelchair because of his severe arthritis.He could not tolerate colchicine because of severe diarrhea and did not want to use steroids.Therefore, the patient was started on interleukin-1 antagonist (anakinra) 100 mg/day subcutaneously.At the end of the first week of therapy, his synovitis regressed significantly and he was able to walk without any support.Because his uric acid level was still high (7.8 mg/dl) with allopurinol (up to 450 mg/day for 3 mos), he was given febuxostat 40 mg/day.After 4 months of therapy with anakinra and febuxostat, he had no active synovitis.He was able to get out of the wheelchair and his CRP (0.4 mg/dl) and uric acid (4.2 mg/dl, the target is < 5 mg/dl) levels were normal.In addition, the ulcerated lesions improved, probably owing to control of the inflammation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".