Barriers to Care in Gout: From Prescriber to Patient
Bibliographic record
Abstract
OBJECTIVE: To explore the understanding of gout and its management by patients and general practitioners (GP), and to identify barriers to optimal gout care. METHODS: Semistructured interviews were conducted with 15 GP and 22 patients in Sydney, Australia. Discussions were focused on medication adherence, experiences with gout, and education and perceptions around interventions for gout. Interviews were audio recorded, transcribed verbatim, and analyzed for themes using an analytical framework. RESULTS: Adherence to urate-lowering medications was identified as problematic by GP, but less so by patients with gout. However, patients had little appreciation of the risk of acute attacks related to variable adherence. Patients felt stigmatized that their gout diagnosis was predominantly related to perceptions that alcohol and dietary excess were causal. Patients felt they did not have enough education about gout and how to manage it. A manifestation of this was that uric acid concentrations were infrequently measured. GP were concerned that they did not know enough about managing gout and most were not familiar with current guidelines for management. For example and importantly, the strategies for reducing the risk of acute attacks when commencing urate-lowering therapy (ULT) were not well appreciated by GP or patients. CONCLUSION: Patients and GP wished to know more about gout and its management. Greater success in establishing and maintaining ULT will require further and better education to substantially benefit patients. Also, given the prevalence, and personal and societal significance of gout, innovative approaches to transforming the management of this eminently treatable disease are needed.
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".