Predictive value of metastatic cervical lymph node ratio in papillary thyroid carcinoma recurrence
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to determine whether the proportion of metastatic cervical lymph nodes resected (metastatic lymph node ratio [MLNR]) predicted papillary thyroid carcinoma (PTC) recurrence, and whether MLNR could alter the predictive ability of TNM nodal classification for recurrence in PTC. METHODS: We conducted a retrospective review of patients with PTC who underwent a total or near-total thyroidectomy with at least 1 lymph node removed at our institution. RESULTS: Of 253 patients, 35 (13.8%) developed recurrent disease. The total MLNR (ratio between total metastatic lymph nodes and total number of lymph nodes resected) independently predicted PTC recurrence (odds ratio [OR], 1.024; 95% confidence interval [CI], 1.010-1.039; p = .001). In receiver operating characteristic (ROC) curve analysis, TNM nodal classification with total MLNR had greater accuracy in predicting PTC recurrence than did TNM nodal classification alone (0.726 and 0.675, respectively). CONCLUSION: MLNR is an independent predictor of PTC recurrence and enhances the predictive value of TNM nodal classification.
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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 | 0.000 | 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 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".