Metabolic evaluation guidelines in patients with nephrolithiasis: Are they being followed? Results of a national, multi-institutional, quality-assessment study
Bibliographic record
Abstract
INTRODUCTION: The significant cost burden of kidney stones underscores the importance of best clinical practice in kidney stone management. We evaluated adherence to kidney stone metabolic evaluation guidelines in a Canadian population and the interest of patients with regard to prevention. METHODS: A questionnaire based on Canadian Urological Association (CUA) best practice guidelines was designed. Patients presenting for extracorporeal shockwave lithotripsy treatment (ESWL) were administered this questionnaire to evaluate risk factors of stone disease and assess the use of metabolic evaluations. Patients were asked if they received explanations about their results and if they were interested in kidney stone prevention. RESULTS: We identified 530 patients at five academic institutions; 79.4% had at least one indication to receive a metabolic evaluation (high-risk stone formers), which increased to 96.6% if first-time stone formers whom reported an interest in metabolic evaluation were included. However, only 41.1 % of these patients had a metabolic evaluation. Endourologists ordered metabolic evaluation more often than other referring urologists (63.6% vs. 36.5%; p<0.001). Furthermore, urologists ordered metabolic evaluations more often than other prescribing physicians (68.9% vs. 31.1%; p<0.001). Sixty-two percent of patients received explanations about their metabolic evaluation results and 77.5% understood them. Regarding prevention, 84.1% and 83.8% were interested in more explanations and in following a diet or taking a medication, respectively. CONCLUSIONS: Adherence to CUA metabolic evaluation guidelines is suboptimal and could be improved by urologists referring patients for ESWL. Communication between physician and patient may not be adequate. The majority of stone formers are interested in kidney stone prevention.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".