Small Hepatocellular Carcinoma With Low Tumor Marker Expression Benefits More From Anatomical Resection Than Tumors With Aggressive Biology
Bibliographic record
Abstract
OBJECTIVE: We assessed prognostic advantage of anatomical resection (AR) over nonanatomical resection (NAR) for hepatocellular carcinoma (HCC) according to multiplication of α-fetoprotein, des-γ-carboxyprothrombin, and tumor volume (ADV) scores. BACKGROUND: Superiority of AR over NAR is debated. ADV score is surrogate marker of postresection prognosis for solitary HCC. METHODS: This study included 1572 patients who underwent curative resection for solitary HCC of 2.0 to 5.0 cm between 2006 and 2014. RESULTS: Preoperative patient profiles were not statistically different between AR and NAR groups. In 1324 naïve patients without preoperative treatment, AR group showed lower recurrence rates (P = 0.003) and higher patient survival rates (P = 0.012) than NAR group. AR group showed lower recurrence rates in patients with ADV ≤5 log (P ≤ 0.046). ADV scores >4 log and >3 log were independent risk factors for tumor recurrence and patient survival in treatment-naïve patients, respectively. In treatment-naïve group with preserved hepatic functional reserve, AR group showed lower recurrence rates in patients with ADV ≤4 log (P = 0.026). Absence of microvascular invasion also showed lower recurrence rates (P = 0.007) in AR group. In 248 patients with preoperative treatment, AR group showed lower recurrence rates (P = 0.001) and higher patient survival rates (P = 0.006). AR group showed lower recurrence rates in patients with ADV ≤4 log (P < 0.001) and higher survival rates in patients with ADV ≤5 log (P ≤ 0.043). CONCLUSIONS: Prognostic benefit of AR was evident in patients with ADV score ≤4 log or absence of microvascular invasion. Patients with less aggressive tumor biology benefit more from AR than NAR, thus being reasonably indicated for AR.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".