Two Approaches to Conservative Neck Dissection: Anterior and Posterior Approaches to Level V
Bibliographic record
Abstract
Background: Papillary thyroid cancer metastasizes to the neck in ~60% of patients, necessitating lateral cervical neck dissection in cases of clinically evident metastasis.1 The nodal pattern of metastasis is to levels II, III, IV, and V, with incidences of 52%, 57%, 41%, and 21%, respectively.2 Given the rate of metastasis to level V, it is recommended to clear level V lymph nodes when performing a lateral neck dissection for metastatic papillary thyroid cancer.3 Two commonly applied approaches exist for dissection of level V depending on the volume of disease present. An anterior approach is used when there is limited disease in level V, and a posterior approach is employed with bulky or extensive disease. This video outlines the two approaches to lateral neck dissection for papillary thyroid cancer metastatic to the lateral neck. Methods: Video was created using iPhone 6 and edited in iMovie in patients undergoing right lateral neck dissection, levels II–V, for metastatic papillary thyroid cancer. An anterior approach is initially demonstrated, followed by a posterior approach in a separate patient. Written consent for video and photography was obtained for both patients. Results: Lateral neck dissection was effectively performed using both the anterior and posterior approaches to level V. Discussion and Conclusions: Clearance of level V lymph nodes is performed through either an anterior or posterior approach when performing lateral neck dissection for metastatic papillary thyroid cancer. It is recommended to employ the anterior approach when there is no clinically evident or limited metastasis in level V. The posterior approach is recommended when there is bulky or extensive disease. No competing financial interests exist. Runtime of video: 8 mins 51 secs Presented at the 2017 World Congress on Thyroid Cancer held in Boston, MA.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".