Safety and cost-effectiveness of immediate right hemicolectomy versus active surveillance for well-differentiated appendiceal neuroendocrine tumors 1-2 cm in size: a Markov decision analysis
Bibliographic record
Abstract
Abstract Purpose Therapeutic management of appendiceal neuroendocrine neoplasms (a-NENs) 10–20 mm in size represents a challenge for both surgeons and oncologists. We compared active surveillance after appendectomy versus immediate right hemicolectomy (RHC). Methods A Markov decision model was developed based on literature parameters. The endpoints were the life expectancy, the quality-adjusted life expectancy (QALY), and the cost-effective ratio expressed by the incremental cost-effective ratio (ICER). A deterministic sample analysis (DSA) with one-way sensitivity analysis was performed for the base case scenario. A probabilistic sensitivity analysis (PSA) was performed to solve the uncertainty of the model. Montecarlo simulation with 100,000 replications for each arm was used. Data are reported in US$. The acceptability of the strategy was set 3 times ($130,049) the Euro area's gross domestic product per capita ($43,394). Results The 10-year survival rates for active surveillance and immediate RHC were 98.2% and 98.9%, respectively. The DSA showed the superiority of active surveillance versus immediate RHC (10 versus 9.0 QALY). Active surveillance costs more than immediate RHC ($35,761 vs. $39,486). The resulting ICER was $4,302 per QALY. The model was more sensitive to the length of follow-up (99.9% variability): the longer the surveillance, the higher the ICER (spread $302,703). PSA analysis confirmed active surveillance as the most cost-effective choice, costing an ICER +$4059 per QALY. Conclusion Active surveillance is safe and cost-effective in patients with appendiceal NENs of 10–20 mm in size, both clinically and economically.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".