Patterns of Lateral Lymph Node Involvement by Neck Level in cNIb Differentiated Thyroid Carcinoma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background/Objectives: The optimal extent of lateral lymph node dissection in cN1b differentiated thyroid cancer remains controversial. This systematic review aimed to assess the frequency of lymph node involvement across neck levels I to V. Materials and Methods: A systematic review was conducted following PRISMA guidelines. PubMed was searched for studies on lateral neck dissection in differentiated thyroid cancer. Included studies reported level-specified metastatic rates. Data on patient numbers and metastatic events were extracted. A random-effects meta-analysis with Freeman–Tukey double arcsine transformation was performed for each neck level to calculate pooled prevalence proportions and 95% confidence intervals. Heterogeneity was assessed using the I2 statistic. Results: Meta-analysis of 57 studies revealed that level III (68%, 95% CI: 63–73) and level IV (66%, 95% CI: 61–70) had the highest metastatic prevalence, followed by level IIA (46%, 95% CI: 37–56). Level V demonstrated an overall prevalence of 22% (95% CI: 18–26), with sublevel VB (19%, 95% CI: 11–28) significantly higher than VA (4%, 95% CI: 1–9). Level I (6%, 95% CI: 2–11) and sublevel IIB (14%, 95% CI: 9–20) showed the lowest risk. Significant heterogeneity (I2 71–94%) was observed across all levels. Conclusions: Our findings support sparing level I, and sublevels IIB and VA during lateral neck dissection. Current guidelines recommend systematic dissection of IIA, III, IV, and VB, although VB involvement was found to be only 19% in our study. Future personalization of the extent of neck dissection, based on individual risk factors, may be key to optimizing oncologic and functional outcomes.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".