Laparoscopic <i>versus</i> open subtotal gastrectomy for gastric adenocarcinoma: cost-effectiveness analysis
Bibliographic record
Abstract
BACKGROUND: Laparoscopic subtotal gastrectomy (LSG) for cancer is associated with good perioperative outcomes and superior quality of life compared with the open approach, albeit at higher cost. An economic evaluation was conducted to compare the two approaches. METHODS: A cost-effectiveness analysis between LSG and open subtotal gastrectomy (OSG) for gastric cancer was performed using a decision-tree cohort model with a healthcare system perspective and a 12-month time horizon. Model inputs were informed by a meta-analysis of relevant literature, with costs represented in 2016 Canadian dollars (CAD) and outcomes measured in quality-adjusted life-years (QALYs). A secondary analysis was conducted using inputs extracted solely from European and North American studies. Deterministic (DSA) and probabilistic (PSA) sensitivity analyses were performed. RESULTS: In the base-case model, costs of LSG were $935 (€565) greater than those of OSG, with an incremental gain of 0·050 QALYs, resulting in an incremental cost-effectiveness ratio of $18 846 (€11 398) per additional QALY gained from LSG. In the DSA, results were most sensitive to changes in postoperative utility, operating theatre and equipment costs, as well as duration of surgery and hospital stay. PSA showed that the likelihood of LSG being cost-effective at willingness-to-pay thresholds of $50 000 (€30 240) per QALY and $100 000 (€60 480) per QALY was 64 and 68 per cent respectively. Secondary analysis using European and North American clinical inputs resulted in LSG being dominant (cheaper and more effective) over OSG, largely due to reduced length of stay after LSG. CONCLUSION: In this decision analysis model, LSG was cost-effective compared with OSG for gastric cancer.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".