Trends in use of lymphadenectomy in surgery with curative intent for intrahepatic cholangiocarcinoma
Bibliographic record
Abstract
BACKGROUND: The role of routine lymph node dissection (LND) in the surgical treatment of intrahepatic cholangiocarcinoma (ICC) remains controversial. The objective of this study was to investigate the trends of LND use in the surgical treatment of ICC. METHODS: Patients undergoing curative intent resection for ICC in 2000-2015 were identified from an international multi-institutional database. Use of lymphadenectomy was evaluated over time and by geographical region (West versus East); LND use and final nodal status were analysed relative to AJCC T categories. RESULTS: Among the 1084 patients identified, half (535, 49·4 per cent) underwent concomitant hepatic resection and LND. Between 2000 and 2015, the proportion of patients undergoing LND for ICC nearly doubled: 44·4 per cent in 2000 versus 81·5 per cent in 2015 (P < 0·001). Use of LND increased over time among both Eastern and Western centres. The odds of LND was associated with the time period of surgery and the extent of the tumour/T status (referent T1a: OR 2·43 for T2, P = 0·001; OR 2·13 for T3, P = 0·016). Among the 535 patients who had LND, lymph node metastasis (LNM) was noted in 209 (39·1 per cent). Specifically, the incidence of LNM was 24 per cent in T1a disease, 22 per cent in T1b, 42·9 per cent in T2, 48 per cent in T3 and 66 per cent in T4 (P < 0·001). AJCC T3 and T4 categories, harvesting of six or more lymph nodes, and presence of satellite lesions were independently associated with LNM. CONCLUSION: The rate of LNM was high across all T categories, with one in five patients with T1 disease having nodal metastasis. The trend in increased use of LND suggests a growing adoption of AJCC recommendations in the treatment of 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".