Practice patterns among thyroid cancer surgeons: Implications of performing a prophylactic central neck dissection
Bibliographic record
Abstract
BACKGROUND: Indications for performing a prophylactic central neck dissection (pCND) in papillary thyroid cancer (PTC) remain controversial. It is unclear how identification of lymph node (LN) metastases should impact the decision to treat with radioactive iodine (RAI). The goals of this study were to identify indications for performing pCND and identify factors that predict the use of adjuvant RAI. METHODS: This was a population based cross-sectional analysis. A prospectively collected database identified 594 patients who underwent total thyroidectomy +/- CND. A multivariate model was constructed to identify indications for pCND and predictors of the use of RAI. RESULTS: 425 CNDs were performed of which 224 were prophylactic. Conventional risk factors (age, tumor size, extra-thyroidal extension) were not associated with performing a pCND. The presence of clinically suspicious lymphadenopathy was the only factor associated with performing CND, thus rendering the CND therapeutic. Positive LNs were retrieved in 39 % of pCND's, upstaging 87 patients. Among all peri-operative predictors of receiving RAI, presence of LN metastases was the strongest predictor [OR = 5.9 (3.7-9.5)], while tumor size was a modest predictor [OR = 1.8 (1.5-2.1)]. Other conventional risk factors did not predict use of adjuvant RAI. CONCLUSIONS: Conventional risk factors were not indications for performing a pCND, implying that the decision was based on individual surgeon preference. Performing pCND upstaged 39 % of patients from cN0 to pN1a, increasing the likelihood of receiving RAI 6-fold. Conventional risk factors were not predictors of receiving adjuvant RAI. This highlights the need for a unified approach to performing a pCND and administering RAI.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".