Cost‐effectiveness of sentinel node biopsy and pathological ultrastaging in patients with early‐stage cervical cancer
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to determine the cost-effectiveness of radical hysterectomy (RH) and sentinel lymph node biopsy (SLNB) for the management of early-stage cervical cancer (stage IA2-IB1). METHODS: A simple decision tree model was developed to follow a simulated cohort of patients with early-stage cervical cancer treated with RH and 1 of 3 lymph node assessment strategies: systematic pelvic lymph node dissection (PLND), SLNB using technetium 99 (Tc99) and blue dye, and SLNB using Tc99 only. SLNB using indocyanine green (ICG) was used as an exploratory strategy. Relevant studies were identified to extract the probability data and utility parameters and to estimate quality-adjusted life-years (QALYs) and absolute life-years (ALYs). Only direct medical costs were modeled, and the time horizon for the study was 5 years. RESULTS: SLNB using Tc99 and blue dye cost $21,089 and yielded 4.54 QALYs and 4.90 ALYs. PLND cost $22,353 and yielded 4.47 QALYs and 4.91 ALYs. SLNB using blue dye and Tc99 was the most cost-effective strategy when ALYs were considered with an incremental cost-effectiveness ratio (ICER) of $144,531. When QALYs were considered, the SLNB technique using Tc99 and blue dye dominated all other strategies. SLNB using ICG cost $20,624 and yielded 4.90 ALYs and 4.54 QALYs. It was clinically superior to and less expensive than all other strategies when QALYs were the outcome of interest and had an ICER of $221,171 per ALY in comparison with RH plus PLND. CONCLUSIONS: SLNB using Tc99 and blue dye with ultrastaging is considered the most cost-effective strategy with respect to 5-year progression-free survival and morbidity-free survival. Although it was included only as an exploratory strategy in this study, SLNB with ICG has the potential to be the most cost-effective strategy. Cancer 2017;123:1751-1759. © 2017 American Cancer Society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".