Influence of Grading on Management and Outcome in Mucoepidermoid Carcinoma of the Parotid—A Multi‐institutional Analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate clinical outcome of low (G1), intermediate (G2), and high-(G3) grade mucoepidermoid carcinomas (MEC) of the parotid gland. STUDY DESIGN: Retrospective chart review including 212 patients. Clinicopathological data was statistically analyzed regarding grading, overall survival (OS), disease-free survival (DFS) and disease-specific survival (DSS). RESULTS: 105 (49.5%) G1, 73 (34.5%) G2, and 34 (16%) G3 MEC were included and 56 (26.4%) patients presented with neck node metastases. The risk of occult nodal metastases was significantly associated with grading and increased from 9.2% in G1 to 26.7% and 27.8% in G2 and G3 tumors, respectively (p = 0.008). Elective periparotid and cervical lymph node dissection was performed in 170 (80.2%) and 70 (33%) patients, respectively. All patients with positive periparotid nodes when subjected to an additional neck dissection had associated cervical neck node involvement (p < 0.001). Grading was an independent significant prognostic factor for OS (HR 4.05; 95%CI: 1.15-14.35; p = 0.030) and DSS (HR 17.35; 95%CI: 1.10-273.53; p = 0.043). In a subgroup analysis, elective neck dissection (END) was also significantly associated with a better DFS (p = 0.041) in neck node-negative G1 MECs. CONCLUSION: The risk of occult nodal metastasis in intermediate-grade MEC is as high as in high-grade MEC and that END in G1 tumors is associated with a prolonged DFS. Additionally, periparotid node involvement seems to be a predictor for positive neck node involvement. This study presents some preliminary data to consider END in clinically neck node negative patients with parotid MEC; however, larger series are needed. LEVEL OF EVIDENCE: 3 Laryngoscope, 133:124-132, 2023.
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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 | 0.003 |
| 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 | 0.000 | 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 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".