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Record W4408050891 · doi:10.1089/end.2024.0673

Economic Burden of Imaging and Interventions in Endourology: A Worldwide Cost Analysis from European Association of Urology Young Academic Urology Endourology and Urolithiasis Working Party

2025· article· en· W4408050891 on OpenAlexaff
Amelia Pietropaolo, Etienne Xavier Keller, Tarık Emre Şener, B. M. Zeeshan Hameed, Arman Tsaturyan, Eugenio Ventimiglia, Patrick Juliebø‐Jones, Christian Beisland, I. Mikoniatis, Lazaros Tzelves, Vincent De Coninck, Frédéric Panthier, Michael Chaloupka, Ewa Bres–Niewada, Alba Sierra, L. Dragoş, Nariman Gadzhiev, Anil Shrestha, Azimdjon Tursunkulov, Khurshid R. Ghani, Chinnakhet Ketsuwan, Alexandre Danilovic, Felipe Pauchard, Hatem Kamkoum, Johan Cabrera, Mariela Corrales, Yazeed Barghouthy, Jia‐Lun Kwok, Theodoros Tokas, Catalina Solano, Pablo Nicolas Contreras, Saeed Bin Hamri, Naeem Bhojani, A. Carolien Bouma-Houwert, Thomas Tailly, Otaš Durutović, Bhaskar Somani

Bibliographic record

VenueJournal of Endourology · 2025
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsUniversité de Montréal
FundersUniversity Hospital Southampton NHS Foundation Trust
KeywordsMedicineUrologyPsychological interventionUrinary stoneUrinary systemInternal medicineNursing

Abstract

fetched live from OpenAlex

Background and Objective: The cost of imaging and interventions in the surgical field varies between countries and sometimes within different regions of the same country. Procedural cost takes into account equipment, consumables, operating room, surgical, anesthetic and nursing teams, radiology, medications, and hospital stay. Health care systems therefore face an incredible burden related to investigations and surgical procedures. The aim of this study was to collect costs of imaging and interventions for kidney calculi across different hospitals and health care systems in the world. Methods: An online shared Google spreadsheet was created by the European Association of Urology Young Academic Urology urolithiasis group. The survey consisted of the cost of four radiological imaging (ultrasound of the urinary tract [USS], plain X-ray radiography of the abdomen including kidneys, ureter, and bladder [XRKUB], noncontrast-enhanced computerized tomography [CTKUB], and contrast-enhanced CT with urographic phase [CTU]) and seven interventions (endoscopic laser treatment of renal stones, ureteroscopic treatment or extraction of ureteral stones, percutaneous nephrolithotomy (PCNL), insertion of ureteral stent, diagnostic ureteroscopy, and cystolitholapaxy). A chosen representative from each country collected and collated the data, and this was converted to Euros (€). Key Findings and Limitations: Data were collected from 32 countries, which include Turkey, Armenia, Nepal, Uzbekistan, Brazil, Chile, Qatar, Peru, Israel, Singapore, Thailand, Colombia, Argentina, Saudi Arabia, Asia, North America, 15 countries from the European continent, and the United States. The mean cost of USS, XRKUB, CTKUB, and CTU was 51.3 € (range: 2–160 €), 27.1 € (range: 2.5–187 €), 105.8 € (range: 19–405 €), and 171.5 € (range: 19–674 €), respectively. Similarly, the cost of endoscopic laser treatment of renal stones, ureteroscopic treatment/extraction of ureteral stones, PCNL, insertion of ureteral stent, diagnostic ureteroscopy, and cystolitholapaxy was 1942.6 € (range: 100–7887 €), 1626.8 € (range: 80–9787 €), 2884.6 € (range: 110–12642 €), 631 € (range: 110–2787 €), 861.6 € (range: 3–2667 €), and 876 € (range: 19–3457 €), respectively. Wide differences in cost between countries were found within the study. Conclusions and Clinical Implications: This study highlights the significant economic impact of kidney stone management on health care systems worldwide. There seem to be significant disparities between costs, and this study shows the social and economic inequalities in health care access, which can differ significantly between private and public health care. These results can aid policymakers to address these disparities and perhaps to learn from other health care providers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.306
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2025
Admission routes1
Has abstractyes

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