Metabolic Stone Screening – Can Education Improve Adherence to National Guidelines?
Bibliographic record
Abstract
Nephrolithiasis is a common urological disease affecting approximately 13% of the global population. Identifying a preventable cause during follow-up for stone formation is a necessary factor in reducing recurrence. The majority of kidney stones are comprised of calcium, with oxalate and phosphate compounds account-ing for approximately 80% of stone formers. Serum analysis can be used to identify the levels of calcium in the blood, excluding metabolic causes for stones. Current NICE guidelines recommend performing a metabolic screen during an acute admission. Adherence to these guidelines from clinicians can be poor. We aim to see whether education can improve serum metabolic requests from clinicians in the UK. Method A case-control study was performed in a single rural district general hospital in the UK. Patients who pre-sented with renal colic were analyzed retrospectively for four months, looking at serum metabolic screen requests. Two months of education was then delivered to acute departments, with a further prospective study performed following this for six months. Outcomes were then compared between both groups to see if there was an improvement in serum requests following the delivery of local education. Results A total of 124 patients were included in our study. 50 patients were analyzed in cohort A over a 4-month period. Complete serum analysis was performed on 22% (n=11) of patients. Incomplete serum analysis was performed on 58% (n-29) of patients with uric acid being missed on all of them. 10 patients had no metabolic serum analysis performed at all, with 1 patient having a previous history of stones. A total of 74 patients were analyzed in cohort B over a 6-month period. This was following the 2 months of departmental education given by urologists. Complete serum analysis was performed on 24% (n=18) of patients. Incomplete serum analysis was performed on 55% (n=41) of patients with uric acid being missed from all of them. The remaining 21% of patients (n=15) had no metabolic serum screen performed at all. There was no significant difference seen between both cohorts after comparing metabolic serum requests before and after department education for 2 months (p=0.7287). Conclusion Improving adherence to clinical guidelines for metabolic screening has proved difficult with departmental teaching alone, with very little improvement achieved. Nevertheless, formal face-to-face education is ad-vocated to reinforce knowledge in junior doctors managing renal stones.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".