Remission in Gout: The Key to Patient Satisfaction?
Bibliographic record
Abstract
Thomas Sydenham (1624–1689) described gout flare pain as a feeling of “dislocated bones,” sometimes akin to the “gnawing of a dog, and sometimes a weight”1. These strong words are not unusual: The majority of gout patients rate their flare pain as severe or very severe, with gout ranking among the top 2 health conditions for its negative effect on patients’ quality of life2. Despite the excruciating pain that patients with gout intermittently suffer, physicians continue to do a poor job of preventing gout flares. Even among patients with severe or very severe symptoms, only 57% of patients with gout are prescribed urate-lowering therapy (ULT)3. Among those who have been prescribed a urate-lowering drug, noncompliance may be as high as 61%4. Multiple factors contribute to these unfortunate statistics. The burden of gout care falls most heavily upon overworked primary care physicians (PCP), who, within an average visit of 15 or 20 minutes, must prioritize the many comorbid conditions common to gout patients, including high … Address correspondence to Dr. M.H. Pillinger, NYU Langone Orthopedic Hospital, 301 E 17th Street, New York, NY 10003, USA. Email: michael.pillinger{at}nyulangone.org.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".