An Observational Study of Gout Prevalence and Quality of Care in a National Australian General Practice Population
Bibliographic record
Abstract
OBJECTIVE: The central strategy for effective gout management is longterm urate-lowering therapy to maintain the serum urate at a level below 0.36 mmol/l. We sought to determine the prevalence of gout and the quality of care in a national Australian general practice population. METHODS: Data were from general practice point-of-care electronic records over a 5-year period (n = 1,479,449). Information was collected on patients with gout according to a validated definition. All patients who visited the same general practices over the study period formed the denominator group. We determined the estimated prevalence of gout, the frequency of allopurinol prescription, and serum urate testing, and the percentage of patients achieving a target serum urate level. RESULTS: The crude prevalence of gout in this general practice population was 1.54% (95% CI 1.52-1.56). Prevalence in men was 2.67% and in women 0.53%. Prevalence increased with age in both men and women (4.90%, 95% CI 4.82-4.99, in men > 65 yrs). Allopurinol was prescribed to 57% of patients with gout during the 5 years of the study. Only 55% of patients with gout had their serum urate tested at any time during the 5-year study period. A target serum urate concentration of < 0.36 mmol/l at any time during the 5-year study period was documented in 22.4% of all people with gout. CONCLUSION: Gout is managed poorly in Australian primary care, with low levels of allopurinol prescribing and serum urate testing. Collectively, these factors probably contribute to low achievement of serum urate targets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".