Cost‐effectiveness analysis of multiple imaging modalities in diagnosis and follow‐up of intermediate complex cystic renal lesions
Bibliographic record
Abstract
OBJECTIVES: To compare health-economic aspects of multiple imaging modalities used to monitor renal cysts, the present study evaluates costs and outcomes of patients with Bosniak IIF and III renal cysts detected and followed-up by either contrast-enhanced computed tomography (ceCT), contrast-enhanced magnetic resonance imaging (ceMRI), or contrast-enhanced ultrasonography (CEUS). PATIENTS AND METHODS: A simulation using Markov models was implemented and performed with 10 cycles of 1 year each. Proportionate cohorts were allocated to Markov models by a decision tree processing specific incidences of malignancy and levels of diagnostic performance. Costs of imaging and surgical treatment were investigated using internal data of a European university hospital. Multivariate probabilistic sensitivity analysis was performed to confirm results considering input value uncertainties. Patient outcomes were measured in quality-adjusted life years (QALY), and costs as averages per patient including costs of imaging and surgical treatment. RESULTS: Compared to the 'gold standard' of ceCT, ceMRI was more effective but also more expensive, with a resulting incremental cost-effectiveness ratio (ICER) >€70 000 (Euro) per QALY gained. CEUS was dominant compared to ceCT in both Bosniak IIF and III renal cysts in terms of QALYs and costs. Probabilistic sensitivity analysis confirmed these results in the majority of iterations. CONCLUSION: Both ceMRI and CEUS can be used as alternatives to ceCT in the diagnosis and follow-up of intermediately complex cystic renal lesions without compromising effectiveness, while CEUS is clearly cost-effective. The economic results apply to a large university hospital and must be adapted for smaller hospitals.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".