The “Acute” Stone Clinic Effect: Improving Healthcare Delivery by Reorganizing Clinical Resources
Bibliographic record
Abstract
OBJECTIVE: To determine the time to specialist urologic consultation and definitive management after establishing a subspecialist administered acute stone clinic (ASC) for adults with symptomatic upper tract stones in a publically funded universal healthcare system. MATERIALS AND METHODS: We retrospectively reviewed 337 adult referrals for stone management. Three distinct 9-week periods were assessed. Group 1 patients were seen/treated by their individual urologist before inception of a general urology emergency clinic (pre-EC). Group 2 patients were seen in a pooled EC and Group 3 patients were seen in the ASC. RESULTS: A total of 337 patients (75, pre-EC; 91, EC; 171, ASC) were reviewed. Mean time to consultation for pre-EC, EC, and ASC cohorts was 29, 7, and 7 days, respectively (p < 0.05), whereas loss to follow-up decreased from 13% to 5% (p < 0.05). On average, the number of patients seen per week increased from 9 to 20. Mean time to stone surgery from date of referral was 75 days pre-EC, 43 days EC, and 25 days ASC (p < 0.05). The percentage of patients undergoing surgery was between 59% and 63% per cohort; however, the number of patients increased from 5 to 11 per week. CONCLUSIONS: By reorganizing clinical resources, a dedicated ASC was able to increase patient capacity, reduce time to urologist consultation and reduce surgical wait times.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".