Evaluating the Cost-Effectiveness of Cervical Cancer Screening and Treatment in Western Romania
Bibliographic record
Abstract
Background and Objectives: As a leading European country in terms of cervical cancer incidence and mortality, there has been a pressing need for Romania to upgrade its cervical cancer management. The criteria set by the International Federation of Gynecology and Obstetrics indicate that different treatments should have a similar trend concerning progression-free survival and overall survival at all the various stages of cervical cancer. This study aimed to assess the cost-effectiveness (CE) of the primary treatment plans related to the survival rate for cervical cancer screening in the western part of Romania and provide some recommendations. Materials and Methods: Descriptive statistics and a correlation model were used to examine costs. AI models have been developed to forecast the CE of different treatments using the above-mentioned studies on overall survival rates and treatment-related toxicity rates for five years. The costs of cervical cancer treatment were sourced from the public health department, the oncology clinic in the western region of Romania, and the County Hospital available for each stage. Results: Treatment expenses vary by cancer stage, with a significant increase from stages IA/IB to IIA, stabilizing between IIA and IIIC (about €7800–€8300), followed by a steep decline in IVA and a more pronounced decrease in IVB and in situ. The results highlight certain treatment combinations and their costs, indicating that the highest costs (exceeding €8000) are linked to multimodal treatments, which encompass surgery, chemotherapy, radiotherapy, and brachytherapy. Conclusions: Advanced cancer stages (IIA–IIIC) entail the highest treatment costs due to intricate, multimodal therapy, whereas early stages (IA, IB, in situ) and late terminal stages (IVB) are linked to considerably reduced treatment costs.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| 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".