Preoperative Systemic Immune-Inflammatory Index Predicts Occult Nodal Disease in Clinically Node-Negative Intrahepatic Cholangiocarcinoma
Bibliographic record
Abstract
BACKGROUND: Accurate preoperative diagnosis of nodal status in intrahepatic cholangiocarcinoma (ICC) remains challenging. The objective of the current study was to determine if the systemic immune-inflammatory index (SII) was associated with occult nodal disease (OND) among cN0 patients undergoing resection for ICC. METHODS: Patients who underwent curative resection for ICC were identified from an international multi-institutional database. A multivariable logistic regression model was used to assess the relationship between SII and OND. RESULTS: Among 490 patients who underwent curative resection with lymph node dissection (LND) for cN0 ICC, 135 (27.6%) had OND. Among these individuals, high SII (≥738.4) was independently associated with OND (odds ratio [OR], 1.85, 95% confidence interval [CI], 1.18-2.92). This association was consistent even among patients with cT1aN0M0 disease (OR, 1.85; 95% CI, 1.19-2.88). Interestingly, among patients with high SII and N0/Nx disease, individuals whose total number of lymph nodes examined (TLNE) was fewer than six had worse 3-year recurrence-free survival (RFS) than patients with a TLNE of six or more (38.8% vs 74.0%; p = 0.002). In contrast, RFS did not differ among patients with low SII and N0/Nx disease (TLNE <6 [49.1%] vs ≥6 [62.4%]; p = 0.099). CONCLUSIONS: High SII was an independent predictor of OND, even among patients with early-stage disease, suggesting that incorporating SII into preoperative risk assessment may refine staging and guide treatment strategies including the need for neoadjuvant therapy as well as the extent and adequacy of LND.
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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 | 0.002 |
| 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".