The prognostic significance of lymph node metastases characteristics in papillary thyroid carcinoma: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Despite neck lymph node metastases occurring commonly in cases of papillary thyroid carcinoma (PTC), there has remained controversy regarding the thresholds that should be used for lymph node characteristics in predicting PTC recurrence risk. AREAS COVERED: This scoping review explored the prognostic significance of lymph node characteristics in predicting risk of PTC recurrence. Following the PRISMA guidelines, the PubMed, Embase, and Web of Science databases were searched and supplemented by hand searching reference lists to identify articles published from database inception until October 2024. A total of 172 studies were included. Most studies had a retrospective cohort design and were reported from South Korea, Japan, and China. In general, an increased number of metastatic lymph nodes (commonly >5), presence of extranodal cancer extension, larger size of metastatic lymph nodes (commonly >3 cm), higher lymph node ratio (commonly ≥0.3 or ≥0.4), and presence of lateral neck compartment nodal metastases were all associated with higher recurrence rates. EXPERT OPINION: The lack of standardized definitions and terminology make the interpretation of findings difficult and limit generalizability. Future studies must focus on exploring long-term recurrence risk, in an exclusively PTC patient population, while also incorporating standardized definitions and terminology for lymph node characteristics and recurrence.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".