Gout and Hospital Admission for Ambulatory Care–Sensitive Conditions: Risks and Trajectories
Bibliographic record
Abstract
OBJECTIVE: To investigate the risks and trajectories of hospital admission for ambulatory care-sensitive conditions (ACSCs) in gout. METHODS: Among individuals aged 35 years to 85 years residing in Skåne, Sweden, in 2005, those with no doctor-diagnosed gout during 1998 to 2005 (n = 576,659) were followed from January 1, 2006, until a hospital admission for an ACSC, death, relocation outside Skåne, or December 31, 2016. Treating a new gout diagnosis (International Classification of Diseases, 10th revision, code M10) as a time-varying exposure, we used Cox proportional and additive hazard models to estimate the effects of gout on hospital admissions for ACSCs. We investigated the trajectory of hospital admissions for ACSCs from 3 years before to 3 years after gout diagnosis using generalized estimating equations and group-based trajectory modeling in an age-and sex-matched cohort study. RESULTS: Gout was associated with a 41% increased rate of hospital admission for ACSCs (hazard ratio 1.41, 95% CI 1.35-1.47), corresponding to 121 (95% CI 104-138) more hospital admissions for ACSCs per 10,000 person-years compared with those without gout. Our trajectory analysis showed that higher rates of hospital admission for ACSCs among persons with gout were observed from 3 years before to 3 years after diagnosis, with the highest prevalence rate ratio (2.22, 95% CI 1.92-2.53) at the 3-month period after diagnosis. We identified 3 classes with distinct trajectories of hospital admissions for ACSCs among patients with gout: almost none (88.5%), low-rising (9.7%), and moderate-sharply rising (1.8%). The Charlson Comorbidity Index was the most important predictor of trajectory class membership. CONCLUSION: Increased risk of hospital admissions for ACSCs in gout highlights the need for better management of the disease through outpatient care, especially among foreign-born, older patients with comorbidities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".