A Decision Analysis of Observation vs Immediate Reintervention for Asymptomatic Residual Fragments Less than 4 mm Following Ureteroscopic Lithotripsy
Bibliographic record
Abstract
INTRODUCTION: We performed a decision analysis model of the cost-effectiveness of observation vs intervention for asymptomatic residual fragments less than 4 mm in diameter following ureteroscopic holmium laser lithotripsy. METHODS: Outcomes data from a retrospective analysis evaluating the natural history, complications and reintervention rates of asymptomatic residual stone fragments performed by the EDGE (Endourology Disease Group for Excellence) Research Consortium were used. A decision analysis model was constructed to compare the cost-effectiveness of initial observation of residual fragments to immediate intervention. Cost of observation included emergency room visits, hospitalizations and reinterventions. The cost analysis model extended to 3 years to account for delayed reintervention rates for fragments less than 4 mm. Costs of emergency department visits, readmissions and reinterventions were calculated based on published figures from the literature. RESULTS: Decision analysis modeling demonstrated that when comparing initial observation to immediate reintervention, the cost was $2,183 vs $4,424. The difference in cost was largely driven by the fact that over 3 years, approximately 55% of all patients remained asymptomatic and did not incur additional costs. This represents an approximate annual per patient savings of $747, and $2,241 over 3 years when observation is selected over immediate reintervention. CONCLUSIONS: Our decision analysis model demonstrates superior cost-effectiveness for observation over immediate reintervention for asymptomatic residual stones less than 4 mm following ureteroscopic lithotripsy. Based on these findings careful stratification and selection of patients may enable surgeons to improve cost-effectiveness of managing small, asymptomatic residual fragments following ureteroscopic lithotripsy.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".