Is Remission a Valid Target for Gout?
Bibliographic record
Abstract
The ideal aim of treatment in any disease is to achieve a cure. This is feasible in most infectious diseases, but in many chronic diseases — such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), or psoriasis — a cure is not yet possible. Available treatments can only hope to decrease disease activity, leading to the disappearance of symptoms and avoiding or minimizing its progression and consequences. This state has received the name of remission; its definition in the Merriam-Webster Medical Dictionary 1 is “a state or period during which the symptoms of a disease are abated.” Remission and cure are, thus, different aims. In the report by Alvarado-de la Barrera, et al 2, in this issue of The Journal , recently developed Preliminary Criteria for the Remission of Gout — which have not undergone further validation — have been applied to patients with gout attending a tertiary center in Mexico. The characteristics of these patients have been described previously3 as coming from a low socioeconomic background and being particularly noncompliant, having poorly managed, very severe gout, and treating their symptoms with self-prescribed glucocorticoid monotherapy4, a therapy associated to the early appearance of … Address correspondence to Prof. E. Pascual, Universidad Miguel Hernandez, Carretera Nacional 332 s/n, 03550, San Juan de Alicante, Alicante, Spain. E-mail: pascual_eli{at}gva.es
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".