Bibliographic record
Abstract
Few arthritides are as painful, incapacitating, and stressful as a severe attack of acute gout, pseudogout, or calcific periarthritis. Successful treatment of these acute microcrystalline events depends on early use of an effective and safe anti-inflammatory drug in full dosage. The sooner such treatment is started the more rapid and complete the response. Treatment options include colchicine, non-steroidal anti-inflammatory drugs (NSAIDs), and corticosteroids including adrenocorticotrophic hormone.1 Although colchicine is traditionally rooted in the treatment of acute gout, in recent years its use has declined steadily.1 Its drawbacks include slow onset of action, narrow ratio of benefit to toxicity, and reduced efficacy when used more than 24 hours after the an attack begins. Colchicine (0.6 mg orally every 2 hours, up to 4-6 mg/day) is now reserved for patients without renal, hepatic, or bone marrow disease, in whom the more effective NSAIDs are contraindicated or poorly tolerated. Intravenous colchicine is best avoided given its potential for serious toxicity, which potentially can result in myelosuppression, hepatic necrosis, renal failure, hypotension, seizures, and death. Intra-articular corticosteroids (for example, methylprednisolone acetate 5-25 mg per joint), systemic corticosteroids (oral prednisone 20 mg/day tapered off over 4-10 days, or intramuscular triamcinolone hexacetonide 60 mg/day, repeated …
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".