Economic model of observation versus immediate resection of hepatic adenomas
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: For small asymptomatic hepatic adenomas (HA), available data are insufficient to establish the superiority of either observation or surgery. We sought to investigate the cost-effectiveness of two initial management strategies. METHODS: We performed a comparative analysis of two theoretical cohorts of 100 patients with small (<5 cm), asymptomatic HA. Discounted cash flow (DCF) models compared the net present value (NPV) of both treatment options at year 10 under three distinct progression rate scenarios. A break-even (BE) analysis was used to determine the BE point at which the NPV for observation and immediate surgery intersect. RESULTS: The NPV for immediate surgery was $1,733,955. The NPV for observation varied between $2,065,315-$2,745,631 for computed tomography (CT), $2,264,575-$2,929,541 for magnetic resonance imaging (MRI), and $802,837-$1,580,413 for ultrasound (US). The BE point was between 6 and 8 years for CT and 5-7 years for MRI. The BE point for US was not reached except in the highest progression rate scenario (12 years). CONCLUSIONS: This study highlights the importance of the underlying progression rate and the cost of imaging when following patients with asymptomatic HA. Overall, US surveillance is the most cost-efficient approach to observing small asymptomatic HA. If cross-sectional imaging is utilized, then immediate surgery is the most cost-effective decision at 5-8 years.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".