Cost-effectiveness of postoperative imaging surveillance strategies for nonfunctional pituitary adenomas after resection with curative intent
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine an optimal follow-up imaging surveillance strategy in terms of cost-effectiveness after resection of nonfunctioning pituitary adenomas with curative intent. METHODS: An individual-level state-transition microsimulation model was used to simulate costs and outcomes associated with three postoperative imaging strategies over a lifetime time horizon: 1) annual MRI surveillance, 2) tapered MRI surveillance (annual surveillance for 5 years followed by surveillance every 2 years), and 3) personalized surveillance (annual surveillance for 5 years followed by surveillance every 2 years when MRI shows remnant disease/postoperative changes, and surveillance at 7, 10, and 15 years for disease-free MRI). Transition probabilities, utilities, and costs were estimated from recent published data and discounted by 3% annually. Model outcomes included lifetime costs (2022 US dollars), quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs). RESULTS: Under base case assumptions, annual surveillance yielded higher costs and lower health effects (QALYs) compared with the tapered and personalized surveillance strategies (dominated). Personalized surveillance demonstrated an additional 0.1 QALY at additional cost ($1298) compared with tapered surveillance (7.7 QALYs at a cost of $12,862). The ICER was $11,793/QALY. The optimal decision was most sensitive to the probability of postoperative changes on MRI after surgery and MRI cost. Accounting for parameter uncertainty, personalized surveillance had a higher probability of being a cost-effective surveillance option compared with the alternative strategies at 79%. CONCLUSIONS: Using standard cost-effectiveness thresholds in the US ($100,000/QALY), personalized surveillance that accounted for remnant disease or postoperative changes on MRI was cost-effective compared with alternative surveillance strategies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".