Predicting Lymph Node Metastasis in T1 Colorectal Cancer Patients Using Interpretable Machine Learning Models: A Multicenter Retrospective Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Whether lymph node metastasis (LNM) is present is crucial for treatment decisions in T1 colorectal cancer (T1 CRC).This study developed predictive models using data from 1,205 patients across seven Chinese medical centers.We evaluated 29 machine learning algorithms and identified CatBoost as the top performer (AUC: 86%, accuracy: 96%).SHAP analysis revealed key predictors of LNM risk, including lymphovascular invasion, age, tumor size, invasion depth, and total lymph node count.Less influential features included perineural invasion and tumor location.The study highlights the importance of retrieving more lymph nodes during surgery to improve staging accuracy.A user-friendly online tool was developed to support clinical decision-making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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