Predicting Lymph Node Metastases in Early Esophageal Adenocarcinoma Using a Simple Scoring System
Bibliographic record
Abstract
BACKGROUND: Endoscopic resection is an organ-sparing option for early esophageal adenocarcinoma, but should be used only in patients with a negligible risk of lymph node metastases (LNM). The objective was to develop a simple scoring system to predict LNM in T1 esophageal adenocarcinoma. STUDY DESIGN: All primary esophagectomies performed for T1 esophageal adenocarcinoma without neoadjuvant therapy at 5 university institutions from 2000 to 2011 were analyzed. Patient and pathologic characteristics were compared between patients with LNM at the time of surgical resection and those without. Univariate and multivariate analyses were performed to establish a simple scoring system that estimated the risk of LNM, using variables from the final surgical pathology. RESULTS: A total of 258 patients were included for analysis (mean age 65.2 years [SD 10.3 years], 88% male). The incidence of LNM was 7% (9 of 122) for T1a and 26% (35 of 136) for T1b. Tumor size (odds ratio [OR] 1.35 per cm, 95% CI 1.07 to 1.71) and lymphovascular invasion (OR 7.50, 95% CI 3.30 to 17.07) were the strongest independent predictors of LNM. A weighted scoring system was devised from the final multivariate model and included size (+1 point per cm), depth of invasion (+2 for T1b), differentiation (+3 for each step of dedifferentiation), and lymphovascular invasion (+6 if present). Total number of points estimated the probability of LNM (low risk [0 to 1 point], ≤ 2%; moderate risk [2 to 4 points], 3% to 6%; and high risk [5+ points], ≥ 7%). CONCLUSIONS: We devised a simple scoring system that accurately estimates the risk of LNM to aid in decision-making in patients with T1 esophageal adenocarcinoma undergoing endoscopic resection.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".