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Record W4416676678 · doi:10.3389/fpubh.2025.1660033

Exploring the cost implications of different treatment modalities for uterine fibroids under the DRG system

2025· article· en· W4416676678 on OpenAlexaff
Linya Huang, Yuxian Nie, Qiuling Shi, Su Yan, Ting Lu, Xite Yang, Yongzeng Lai, Xiaofeng Zhu

Bibliographic record

VenueFrontiers in Public Health · 2025
Typearticle
Languageen
FieldMedicine
TopicUterine Myomas and Treatments
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsUterine fibroidsModalitiesMedical recordPaymentHealth careDiagnosis-related groupHysterectomyTotal cost

Abstract

fetched live from OpenAlex

This paper, based on data from inpatient medical records with uterine fibroids (ICD-10: D25) from the medical record homepages of secondary and higher-level hospitals in Sichuan Province between 2016 and 2024, investigated differences in medical resource consumption and costs between high-intensity focused ultrasound (HIFU) and traditional surgical treatments under the diagnosis-related group (DRG) system. Cases were classified using the MS-DRG grouper into groups with and without complications or comorbidities (CC/MCC). An XGBoost model was employed to reclassify data for HIFU patients, addressing missing coding. Group validity was assessed using the coefficient of variation (CV) and reduction in variance (RIV). Factors influencing costs were identified via multifactorial regression analysis. Results showed that in the group without CC/MCC, HIFU treatment significantly reduced the length of hospital stay, decreased the proportion of consumables costs and medication costs, but increased the proportion of treatment costs. Median hospitalization costs were significantly higher in the CC/MCC group than in the non-CC/MCC group. Multifactorial regression analysis identified length of stay (LOS), HIFU treatment, and CC/MCC grouping as key cost drivers. Additionally, costs for patients covered by Urban Employee Basic Medical Insurance and Commercial Health Insurance were significantly higher than those with other payment types. This paper confirms the effectiveness of DRG grouping in reflecting resource consumption disparities and reveals the potential of HIFU technology for optimizing medical resource allocation. Recommendations include promoting HIFU adoption, optimizing medical insurance payment policies, and strengthening hospital management to achieve dual goals of cost control and healthcare quality improvement. The findings provide empirical evidence for DRG payment reform and the selection of uterine fibroid treatment modalities.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.133
GPT teacher head0.351
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2025
Admission routes1
Has abstractyes

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