Assessment of the economic relevance of the use of single-use digital flexible ureteroscopes
Bibliographic record
Abstract
INTRODUCTION: Breakages and repairs related to flexible digital reusable ureteroscopes (flURS) are expensive. Thus, we aimed to assess the cost-effectiveness of single-use flexible digital ureteroscopes ureteroscopes (SUFDU). METHODS: We conducted a literature review on MEDLINE and EMBASE until September 19, 2018. Systematic reviews and guidelines were assessed for methodologic quality by using standardized grids (R-AMSTAR and AGREE-II). Original studies were analyzed according to local customized grids. The Critical Appraisal Skills Program (CAPS) tool enabled the assessment of the economic aspects in the literature. We also collected local data over a year in 2017-2018 and conducted an economic evaluation by cost minimization, comparing SUFDU and flURS in our center. By generating different flURS breakage reduction scenarios, we aimed to demonstrate the budgetary impact SUFDU introduction would have in our center. RESULTS: Five economic studies were included. Data on flURS showed breakage rates between 6.4-13.2%, and mean numbers of interventions before breakage of 7.5-14.4. Four of the five economic analyses suggested a higher cost per intervention with SUFDU. Our local data demonstrated similar results (6.4% and 11.8 cases) and enabled us to estimate the annual number of ureteroscopies for which SUFDU would become profitable: 11-26 (depending on the chosen device). Furthermore, we illustrated how selective use of SUFDU can reduce annual costs by avoiding breakages in different scenarios. CONCLUSIONS: The mean cost per intervention with SUFDU is usually higher than with flURS in high-volume centers and exclusive use becomes unprofitable from a small number of cases.
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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 | 0.072 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".