Clinical Parameters Predicting Survival Duration after Hepatectomy for Intrahepatic Cholangiocarcinoma
Bibliographic record
Abstract
BACKGROUND: Currently, the most effective treatment for intrahepatic cholangiocarcinoma (ICC) is complete hepatic tumour excision. OBJECTIVE: To identify the clinical parameters associated with survival duration for ICC patients following hepatectomy, and to construct a mathematical model for predicting survival duration. METHODS: Demographic data and clinical variables for 102 patients diagnosed with ICC, who underwent exploratory laparotomy at a single centre from July 1998 to December 2000 and were followed for an average of 24 months, were collected in 2011. Patients were randomly assigned into training (n=76) and validation (n=26) groups. Univariate and multivariate analyses were performed to identify factors associated with posthepatectomy survival duration. RESULTS: Univariate analysis revealed that more than three lymph node metastases, a serum carbohydrate antigen 19-9 level greater than 37 U⁄mL, stage IVa tumours, and intra- or perihepatic metastases were significantly associated with decreased survival duration. Curative resection was significantly associated with increased survival duration. A mathematical model incorporating parameters of age, sex, metastatic lymph node number, curative surgery, carbohydrate antigen 19-9 concentration, alpha-fetoprotein concentration, hepatitis B, TNM stage and tumour differentiation was constructed for predicting survival duration. For a survival duration of less than one year, the model exhibited 93.8% sensitivity, 92.3% total accuracy and a positive predictive value of 93.8%; for a survival duration of one to three years, the corresponding values were 80.0%, 69.2% and 57.1%, respectively. CONCLUSIONS: The mathematical model presented in the current report should prove to be useful in the clinical setting for predicting the extent to which curative resection affects the survival of ICC patients, and for selecting optimal postoperative treatment 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 | 0.001 | 0.004 |
| 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".