Sentinel Lymph Node Biopsy in Thyroid Cancer
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to retrospectively assess the specific perils associated with conducting sentinel lymph node biopsies to determine whether a central compartment neck dissection (CCND) is necessary in well-differentiated thyroid cancer. The goal was to assess the specific reasons for a false negative in 3 specific patients among a large population of thyroidectomy patients. STUDY DESIGN: Case series with chart review. SETTING: Three McGill University teaching hospitals that are part of the McGill University Thyroid Cancer Center in Montreal, Quebec, Canada. SUBJECTS: Patients undergoing thyroidectomy and CCND for nodules suspicious for thyroid cancer (June 2009 to May 2010). METHODS: Retrospective analysis of 157 patients who underwent thyroidectomy and analysis of CCND as a function of sentinel lymph node status on frozen section as determined by a pathologist at one of the participating centers. RESULTS: Three patients were considered true failures or false negatives of the original protocol. These 3 patients were deemed to have benign lymph node status intraoperatively but were found postoperatively to harbor malignancy and therefore should have undergone CCND. The critical reasons for the imperfect false-negative rate are believed to be secondary to samples falsely deemed benign as well as multinodular disease. CONCLUSION: The value of sentinel lymph node biopsy in thyroid cancer, although largely debated, appears to be strong. If caution is taken in using dedicated head and neck pathologists for sentinel lymph node cases, as well as properly addressing multinodular malignancy, clinical decision making can be rendered more objective.
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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) | 0.001 | 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 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".