The conditional recurrence-free survival after R0 hepatectomy for locally advanced intrahepatic cholangiocarcinoma: A competing risk analysis based on inflammation-nutritional status
Bibliographic record
Abstract
Background: Conditional survival analysis can serve as a dynamic prognostic metric, which helps to estimate the real-time survival probability over time. The present study conducted a conditional recurrence-free survival (CRFS) analysis for locally advanced intrahepatic cholangiocarcinoma (ICC) after R0 hepatectomy from an inflammatory-nutritional perspective using the competing risk method. Methods: We extracted the medical data of 164 locally advanced ICC patients after R0 resection from Sun Yat-sen University Cancer Center. The calculation formula of the CRFS rate is CRFS(y/x) = RFS(y + x)/RFS(x). Univariable and multivariable COX regression analysis and competing risk analysis were conducted to identify RFS indicators. Results: Considering death before recurrence as a competing risk factor, the conditional RFS rates every 6 months gradually increased over time. The 24-month RFS rate increased from 29.2 % to 49.9 %, 68.5 %, and 85.1 % given 6, 12, and 18-month already recurrence-free survival, respectively. Both in multivariate COX regression analysis and competing risk analysis, tumor diameter and number, lymph node metastasis, aggregate systemic inflammation index score (AISI), and albumin-bilirubin score (ALBI) all remained significant. For both AISI and ALBI variables, the CRFS rates in the low-value set were higher than those of the high-value set. Conclusions: Conditional RFS rates of locally advanced ICC after R0 hepatectomy dynamically increased over time, which contributed to reducing survivors' psychological distress and facilitating personalized follow-up schedules. In addition, a person's inflammatory and nutritional status significantly impact the recurrence risk. Oncologists should consider the role of inflammation-nutritional status when making decisions for patients with locally advanced ICC.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| 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 | 0.000 | 0.001 |
| 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".