Impact of facial nerve resection in parotid cancer abutting the facial nerve without preoperative paralysis: A multicentric propensity score-based analysis
Bibliographic record
Abstract
OBJECTIVES: The management of the facial nerve (FN) is a major issue in parotid cancer, especially when there is no preoperative facial palsy and FN invasion is discovered intraoperatively. The aim of this study was to assess the impact of FN resection in patients with parotid cancer abutting the FN, without pretreatment facial palsy, using a propensity score matching. MATERIALS AND METHODS: Data from all patients treated between 2009 and 2020 for a primary parotid cancer abutting or invading the FN but without pretreatment facial palsy were extracted from the national multicentric REFCOR database. Three different definitions of tumors abutting the FN were used for sensitivity analyses, in a retrospective setting. Propensity score matching was used to assess the impact of FN resection on disease-free survival (DFS), overall survival (OS) and locoregional recurrence-free survival (LRRFS). RESULTS: A total of 163 patients with parotid cancer abutting or invading the FN without pretreatment facial palsy were included. Among them, 99 patients (61 %) underwent FN resection. After overlap weighting and multiple imputation, no benefit of FN resection over preservation was found in terms of OS (HR = 1.21, p = 0.6), DFS (HR = 0.88, p = 0.5) and LRRFS (HR = 0.99, p = 1). Sensitivity analyses revealed similar results, and no significant efficacy was found in the subgroup analyses. CONCLUSION: In this retrospective study with propensity score analysis, FN resection did not improve survival outcomes in patients without preoperative facial palsy treated surgically for a primary parotid cancer abutting the FN. In line with recent guidelines, the results of this study suggest that FN preservation should be considered whenever possible in this specific group of patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".