Cost-effectiveness of direct surgery versus preoperative octreotide therapy for growth-hormone secreting pituitary adenomas
Bibliographic record
Abstract
PURPOSE: The objective of this study was to compare the cost-effectiveness of preoperative octreotide therapy followed by surgery versus the standard treatment modality for growth-hormone secreting pituitary adenomas, direct surgery (that is, surgery without preoperative treatment) from a public third-party payer perspective. METHODS: We developed an individual-level state-transition microsimulation model to simulate costs and outcomes associated with preoperative octreotide therapy followed by surgery and direct surgery for patients with growth-hormone secreting pituitary adenomas. Transition probabilities, utilities, and costs were estimated from recent published data and discounted by 3% annually over a lifetime time horizon. Model outcomes included lifetime costs [2020 United States (US) Dollars], quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratios (ICERs). RESULTS: Under base case assumptions, direct surgery was found to be the dominant strategy as it yielded lower costs and greater health effects (QALYs) compared to preoperative octreotide strategy in the second-order Monte Carlo microsimulation. The ICER was most sensitive to probability of remission following primary therapy and duration of preoperative octreotide therapy. Accounting for joint parameter uncertainty, direct surgery had a higher probability of demonstrating a cost-effective profile compared to preoperative octreotide treatment at 77% compared to 23%, respectively. CONCLUSIONS: Using standard benchmarks for cost-effectiveness in the US ($100,000/QALY), preoperative octreotide therapy followed by surgery may not be cost-effective compared to direct surgery for patients with growth-hormone secreting pituitary adenomas but the result is highly sensitive to initial treatment failure and duration of preoperative treatment.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".