The 3-Year Incidence of Gout in Elderly Patients with CKD
Bibliographic record
Abstract
Background and objectives The risk of gout across CKD stages is not well described. Design, setting, participants, & measurements We performed a retrospective cohort study using linked health care databases from Ontario, Canada from 2002 to 2010. The primary outcome was the 3-year cumulative incidence of gout, on the basis of diagnostic codes. We presented our results by level of kidney function (eGFR≥90 ml/min per 1.73 m 2 , 60–89, 45–59, 30–44, 15–29, and chronic dialysis) and by sex. Additional analyses examined the risk of gout adjusting for clinical characteristics, incidence of gout defined by the receipt of allopurinol or colchicine, and gout risk in a subpopulation stratified by the level of eGFR and albuminuria. Results Of the 282,925 adults aged ≥66 years, the mean age was 75 years and 57.9% were women. The 3-year cumulative incidence of gout was higher in older adults with a lower level of eGFR. In women, the 3-year cumulative incidence of gout was 0.6%, 0.7%, 1.3%, 2.2%, and 3.4%, and in men the values were 0.8%, 1.2%, 2.5%, 3.7%, and 4.6%, respectively. However, patients on chronic dialysis had a lower 3-year cumulative incidence of gout (women 2.0%, men 2.9%) than those with more moderate reductions in kidney function ( i.e. , eGFR 15–44 ml/min per 1.73 m 2 ). The association between a greater loss of kidney function and a higher risk of diagnosed gout was also evident after adjustment for clinical characteristics and in all additional analyses. Conclusions Patients with a lower level of eGFR had a higher 3-year cumulative incidence of gout, with the exception of patients receiving dialysis. Results can be used for risk stratification.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".