Improving Gout Care in a Canadian Academic Medical Center Through a Multidisciplinary, Nurse-Led Protocol
Bibliographic record
Abstract
Objective Following Health Canada’s knowledge translation framework, we report the results of a clinical audit from 2012 to 2015 followed by a multidisciplinary, nurse-led gout care protocol with a treat-to-target (T2T) strategy implemented in April 2018. Methods A clinical audit with chart reviewing was completed for adults with gout and urate-lowering therapy (ULT) indication at the Centre Hospitalier Universitaire de Sherbrooke. A nurse-led treatment algorithm using allopurinol was then developed. Titration of ULT by a nurse every 4 weeks was done until serum uric acid (SUA) target. In the postprotocol implementation, adults with gout and ULT indication were retrospectively recruited through a billing agency until December 2020. The main outcome was SUA target achievement at 6 months. Results Of 50 patients identified in the audit, 31% reached SUA target at 6 months and 16% were lost to follow-up. A 74-patient postprotocol implementation cohort was recruited, with 43 in the protocol group and 31 under usual care. Most prevalent ULT indication was ≥ 2 gout attacks per year (n = 52) at 70%. Target SUA was reached in 65% (n = 28) in the protocol group at 6 months compared to 19% (n = 6) in the usual care group (P< 0.001). Failing to titrate medication in the usual care group was the leading cause for nonachievement of SUA target at 6 months. Five percent of patients were lost to follow-up, all in the usual care group. Conclusion A multidisciplinary, nurse-led protocol with a T2T strategy implemented after a clinical audit significantly improved gout care. Such protocol could be replicated elsewhere in Canada.
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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 | 0.087 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".