Management of Solitary 1 cm to 2 cm Liver Nodules in Patients with Compensated Cirrhosis: A Decision Analysis
Bibliographic record
Abstract
OBJECTIVES: Current guidelines, based on expert opinion, recommend that suspected 1 cm to 2 cm hepatocellular carcinoma (HCC) detected on screening be biopsied and, if positive, treated (eg, resection or transplantation). Alternative strategies are immediate treatment or observation until disease progression occurs. METHODS: A Markov decision model was developed that compared three management strategies - immediate resection, biopsy and resection if positive, and ultrasound surveillance every three months until disease progression - for a single 1 cm to 2 cm liver nodule suspicious for HCC following ultrasound screening and computed tomography confirmation. The cohort included 55-year-old patients with compensated cirrhosis and no significant comorbidities. The model used in the present study incorporated the probabilities of false-positive and false-negative results, needle-track seeding, HCC recurrence, cirrhosis progression and death. The quality-adjusted life expectancy (LE) and the unadjusted LE were evaluated and the model's strength was assessed with sensitivity analyses. RESULTS: In the base case analysis, biopsy, resection and surveillance yielded an unadjusted LE of 60.5, 59.7 and 56.6 months, respectively, and a quality-adjusted LE of 46.6, 45.6 and 43.8 months, respectively. In probabilistic sensitivity analyses, biopsy was the preferred strategy 69.5% of the time, resection 30.5% of the time and surveillance never. Resection was the optimal decision if the sensitivity of biopsy was very low (less than 0.45) or if the accuracy of the imaging tests resulted in a high percentage of HCC-positive patients (greater than 76%) in the screened cohort, as with expert interpretation of triphasic computed tomography. CONCLUSIONS: The present model suggests that biopsy is the preferred management strategy for these patients. When postimaging probability of HCC is high or pathology expertise is lacking, resection is the best alternative. Surveillance is never the optimal strategy.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| 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".