Utility of level VI neck dissection in diagnostic hemithyroidectomies.
Bibliographic record
Abstract
BACKGROUND: The utility and safety of level VI central compartment lymph node dissection (LND) for the early detection of lymph node (LN) involvement during diagnostic hemithyroidectomy, for the evaluation of suspicious thyroid nodules, has yet to be established in the literature. METHODS: A retrospective review of all patients who underwent diagnostic hemithyroidectomy with level VI LND from a large head and neck oncology program from October 1, 2001, to May 10, 2009, was performed. RESULTS: A consecutive series of 78 patients were reviewed. Twenty-six patients (29.8%) were diagnosed with malignant neoplasm. All patients with malignant LNs (n = 5; 6.4%) were diagnosed with papillary carcinoma. On average, 4.8 LNs were found through neck dissection in patients with positive nodes compared to 2.4 LNs in those without lymph node involvement (p = .04). No postoperative adverse events in the patient group were attributed to the level VI neck dissection. CONCLUSIONS: In patients undergoing diagnostic hemithyroidectomies, routine level VI LND was able to identify LN metastases in 6.4% of patients. The number of LNs was a strong predictor of positive node disease. Minimal surgical risks are associated with this procedure, and surgeons may avoid the risks of level VI reexploration in subsequent completion thyroidectomy.
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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 | 0.001 | 0.006 |
| 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".