Cost‐effectiveness of the endoscopic versus microscopic approach for pituitary adenoma resection
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To evaluate the cost-effectiveness of an endoscopic versus microscopic approach to pituitary adenoma resection. STUDY DESIGN: Markov decision tree economic evaluation. METHODS: An economic evaluation using a Markov decision tree model was performed. The economic perspective was that of the healthcare third-party payer. Effectiveness and probability data were obtained from a single meta-analysis of 38 studies. Costs were obtained from the Healthcare Cost and Utilization Project database and wholesale pharmaceutical pricing. Multiple sensitivity analyses were performed including a probabilistic sensitivity analysis. Comparative treatment groups were: 1) endoscopic approach and 2) microscopic approach to pituitary adenoma resection. The primary outcome was cost per quality-adjusted life year (QALY). The time horizon was 25 years, and costs were discounted at a rate of 3.5%. RESULTS: The endoscopic approach cost a total of $17,244.63 and produced a total of 24.30 QALYs. The microscopic approach cost a total of $23,756.60 and produced a total of 24.20 QALYs. In the reference case, the endoscopic approach was a dominant intervention (both less costly and more effective); therefore, an incremental cost-effectiveness ratio was not calculated. The sensitivity analysis demonstrated 79% certainty that the endoscopic approach is the cost-effective decision, at a willingness to pay threshold of $50,000 per QALY. CONCLUSIONS: This economic evaluation suggests that the endoscopic approach is the more cost-effective intervention compared to the microscopic approach for patients requiring a pituitary adenoma resection.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".