Preoperative prediction of lymph node metastasis risk in papillary thyroid carcinoma based on multiple model comparisons
Bibliographic record
Abstract
The clinical necessity of lymph node dissection in papillary thyroid carcinoma (PTC) surgery remains contentious. This study compared four logistic regression (LR) models (with distinct feature selection strategies) and four machine learning (ML) models to preoperatively predict lymph node metastasis (LNM) risk in PTC patients, with emphasis on multidimensional evaluation and cross-populational generalizability. Data from 3,175 PTC patients (2021 cohort) were randomly split into training (70%) and testing (30%) subsets, with external validation performed using a Chinese (2024, n = 104) and a Canadian (2019-2022, n = 412) cohort. Twelve predictors were screened, and models were evaluated using metrics of discrimination (AUC), calibration (Brier Score), classification accuracy, and clinical utility. The prevalence of LNM was 34.48%, 36.54%, and 30.10% in the internal, Chinese, and Canadian cohorts, respectively. Among ML models, Random Forest achieved the highest internal AUC (0.767), whereas XGBoost demonstrated superior generalization (external AUCs: 0.785 and 0.725). LR models, particularly BestSubset-GLM, outperformed these ML models with an internal AUC of 0.770 and external AUCs of 0.831 and 0.785. Notably, BestSubset-GLM exhibited high specificity (0.86), precision (0.59), favorable calibration (Brier Score < 0.20), and robust clinical utility across the approximately 15-90% threshold probability range. Extrathyroidal extension, tumor size above 1.00 cm, younger age, and male gender were identified as key LNM risk factors. Bethesda classification and molecular aberrations were integrated into models. BestSubset-GLM balanced parsimony, interpretability, and generalizability, thereby supporting clinical decision-making through dynamic nomograms. Comprehensive evaluation beyond AUC is crucial.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".