The Role of Sentinel Lymph Node Biopsy in Differentiated Thyroid Carcinoma
Bibliographic record
Abstract
OBJECTIVE: To determine whether sentinel lymph node (SLN) biopsy can accurately predict central compartment metastasis in patients with differentiated thyroid carcinoma. DESIGN: Prospective clinical study. SETTING: Academic tertiary care center. PATIENTS: Ninety-eight patients (82 women and 16 men; mean age, 48.3 years) underwent a total thyroidectomy and central compartment dissection. INTERVENTION: Peritumoral injection of methylene blue dye, 1%, followed by SLN biopsy. MAIN OUTCOME MEASURES: The final pathology report established the presence of metastasis among SLNs and lymph nodes that did not stain blue (non-SLNs [NSLNs]). RESULTS: Differentiated thyroid carcinoma was found in 75 of 98 patients (77%). Seventy of 75 patients with differentiated thyroid carcinoma presented with SLNs and/or NSLNs within the central compartment. Fifteen of 70 patients had metastasis-positive SLNs, while 55 had metastasis-negative SLNs. Six of 15 patients with positive SLNs also had positive NSLNs. No patients with negative SLNs were found to have positive NSLNs. Sentinal lymph node status was a highly significant predictor of NSLN result (Fisher exact test, P < .001). The accuracy, sensitivity, specificity, and positive and negative predictive values of SLN biopsy were 87%, 100%, 86%, 40%, and 100%, respectively. CONCLUSIONS: To our knowledge, this is the largest series of SLN biopsy in patients with differentiated thyroid carcinoma. Our experience suggests that this is an accurate and noninvasive means to identify subclinical lymph node metastasis. Because negative SLNs correlate strongly with a negative central compartment (100% in this study, P < .001), this technique can be used as an intraoperative guide when determining the extent of surgery necessary in cervical level VI.
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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 | 0.002 | 0.009 |
| 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) | 0.001 | 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".