Quality Indicators of Central Compartment Neck Dissection in Thyroid Surgery
Bibliographic record
Abstract
OBJECTIVES: Quality metrics are an increasingly important means of improving patient care. Variability in the number of lymph nodes removed during central compartment lymph node dissection (CCLND) at the time of thyroidectomy has not been studied. STUDY DESIGN: A retrospective cohort study was performed using American College of Surgeons National Quality Improvement Program (ACS-NSQIP) data. SETTING: Centers in North America and worldwide contributing data to ACS-NSQIP and performing thyroidectomy on adults in inpatient and outpatient settings were included. SUBJECTS AND METHODS: Adult patients undergoing thyroidectomy with or without CCLND were included. Outcomes of interest were number of nodes removed during CCLND and risks of postoperative hypocalcemia. RESULTS: In total, 6108 patients met inclusion criteria (1565 with CCLND). The median number of lymph nodes removed during CCLND was 2. There was no statistically significant association between postoperative hypocalcemia and CCNLD, regardless of number of nodes removed. However, we were underpowered to detect this association based on the overall low nodal yield of many CCLNDs performed. CONCLUSION: In many cases where CCLND is documented as part of thyroidectomy, very few lymph nodes are removed. Our ability to draw conclusions regarding the effect of CCLND on postoperative hypocalcemia is restricted due to the limited nature of many CCLNDs performed.
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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 | 0.000 |
| 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) | 0.000 | 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".