Assessment of nodal staging and risk factors for nodal involvement in gallbladder cancer
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Nodal assessment in gallbladder cancer remains challenging, particularly in incidental gallbladder cancer. This understages the number of patients with node-positive disease, resulting in prognostic inaccuracy and insufficient adjuvant treatment. This study aimed to identify risk factors for positive nodes in gallbladder cancer and to compare prognostic discrimination of available nodal staging parameters. METHODS: This international cohort study assessed gallbladder cancer resections undertaken between 1 January 2010 and 31 December 2020. Logistic regression was used to identify risk factors for node-positive status and develop a risk prediction score for positive nodes. Nodal staging models, including nodal site, number of positive nodes, and positive node ratio were compared for greatest prognostic discrimination in gallbladder cancer. RESULTS: A total of 3676 patients underwent gallbladder cancer resection across 133 centres in 41 countries. Tumour (T) stage (T2, P = 0.012; T3, P = 0.002; and T4, P < 0.001), lymphovascular and perineural infiltration (P < 0.001), and tumour differentiation (P < 0.001) carried the greatest risk of positive nodes. These three parameters comprised the OMEGA Node Positivity Prediction Score (OMEGA-NOPPS) with C-statistics of 0.81 (95% confidence interval 0.78 to 0.84) in the training data set and 0.79 (0.73 to 0.85) in the test data set for identification of node-positive status, highlighting a ≥ 20% increased risk of positive nodes in poorly differentiated tumours with lymphovascular and perineural infiltration despite T1 disease. CONCLUSION: Data from this large multicentre study confirmed that the number of positive nodes is the most discriminative prognostic model for nodal staging in gallbladder cancer. OMEGA-NOPPS provides three simple parameters to stratify nodal involvement according to risk. Incidental gallbladder cancer with lymphovascular and perineural infiltration and poorly differentiated tumours, including early T stages, should be considered for further treatment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it