The Small Acute Ureteral Stone Protocol: Clinical Outcomes and Relapse Patterns
Bibliographic record
Abstract
Purpose: Nephrolithiasis affects approximately 10% of North Americans, placing a significant burden on health care systems. This study evaluates the effectiveness of a novel, virtual Small Acute Ureteral Stone (SAUS) protocol for managing ureteral stones ≤5 mm, aiming to optimize resource utilization and patient care. Materials and Methods: A retrospective review was conducted on 209 consecutive patients enrolled in the SAUS protocol from June 2018 to May 2019. The protocol included follow-up renal bladder ultrasound and nurse case manager telephone assessment. Patients were followed for a median of 5.4 years, with data collected on stone passage rates, interventions, and long-term outcomes. Results: The SAUS protocol successfully redirected 53% of patients from urgent clinic visits. Of these, 98% did not necessitate urologic intervention for their small ureteral stone. Overall, 77% of patients showed radiographical confirmation of stone passage, and 74% reported being symptom-free. Only 13% of all patients underwent intervention for their initial ureteral stone. Long-term follow-up revealed that after discharge from our protocol, 67% of patients did not re-present over 5 years, and 90% remained free from urologic intervention. The study’s retrospective nature and reliance on electronic medical records may have introduced bias. Patient adherence to follow-up recommendations varied, potentially affecting outcome accuracy. Conclusion: The SAUS protocol demonstrates effectiveness in virtually managing small ureteral stones, reducing unnecessary clinic visits and interventions. The protocol’s success suggests its potential for implementation in similar clinical scenarios, potentially reducing health care costs and improving patient care in urolithiasis management.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".