Extent of central neck dissection among thyroid cancer surgeons: Cross‐sectional analysis
Bibliographic record
Abstract
BACKGROUND: It is unclear if surgeons are performing comprehensive central neck dissections for well-differentiated thyroid cancer. The purpose of this study was to determine mean lymph node retrieval in central neck dissection as well as variability across surgeons and institutions. METHODS: A prospectively collected database identified 18 surgeons performing 425 central neck dissections, 313 unilateral and 112 bilateral. Demographics, perioperative, and pathologic factors were analyzed. RESULTS: Mean lymph node yield was 7.4 and 11.9 for unilateral and bilateral central neck dissection, respectively. Although 224 central neck dissections were prophylactic, both total and pathologic lymph node yields were significantly higher in therapeutic central neck dissection. There was a significant variation in lymph node yield across individual surgeons, institutions, and regions. High-volume central neck dissection surgeons have significantly lower lymph node yield compared to low-volume surgeons. CONCLUSION: Central neck dissection seems to be performed adequately; however, there is a significant variation in lymph node yield. Future initiatives should try to standardize the central neck dissections performed, with emphasis on obtaining a sufficient yield. © 2015 Wiley Periodicals, Inc. Head Neck 38: E328-E332, 2016.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".