Any link of gout disease control among hypertensive patients and onset of end-stage renal disease? Results from a population-based study
Bibliographic record
Abstract
AIMTo determine the impact of allopurinol non-adherence as a proxy for uncontrolled disease on primary prevention of end-stage renal disease (ESRD). METHODSA cohort of 2752 patients with gout diagnosis was reconstructed using the Québec Régie de l'assurance maladie du Québec and MedEcho administrative databases.Eligible patients were new users of allopurinol, aged 45-85, with a diagnosis of hypertension, and treated with an antihypertensive drug between 1997 and 2007. RESULTSMajor risk factor for ESRD onset was chronic kidney disease at stages 1 to 3 [rate ratio (RR) = 8.00; 95% confidence interval (CI): 3.16-22.3and the severity of hypertension (≥ 3 vs < 3 antihypertensives)] was a trending risk factor as a crude estimate (RR = 1.94; 95%CI: 0.68-5.51).Of 341 patients, cases (n = 22) and controls (n = 319), high adherence level (≥ 80%) to allopurinol therapy, compared with lower adherence level (< 80%), was associated with a lower rate of ESRD onset (RR = 0.35; 95%CI: 0.13-0.91). CONCLUSIONGout control seem to be associated with a significant
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".