Surgical Management of Nonmalignant Parotid Masses in the Pediatric Population: The Montreal Children's Hospital's Experience
Bibliographic record
Abstract
Nonmalignant parotid masses in children can have protean etiologies ranging from infective parotitis to a benign neoplastic, vascular, or congenital origin. We review the 10-year experience of a tertiary care pediatric centre with respect to the surgical management of nonmalignant parotid masses. In total, 15 patients with nonmalignant masses of the parotid gland region underwent surgery. Five children were diagnosed with lymphoepithelial cyst or first branchial cleft cyst. Three children were diagnosed with parotid abscess, one of whom had atypical mycobacteria. Other diagnoses included lymphangioma (three cases), chronic inflammation (two cases), and epidermoid cyst (one case). One patient who presented with a parotid cyst was diagnosed postoperatively with plexiform neurofibroma of the facial nerve. She was the only patient with postoperative facial nerve paresis, affecting the orbital branch. Presentation and postoperative complications of these surgically managed nonmalignant parotid masses are reviewed. The history and physical examination are of the utmost importance in predicting the diagnosis, although ultrasonography and computed tomography can be useful. Fine-needle aspiration cytology was not well tolerated by children and appears of little use as the accurate diagnosis was provided by the surgical pathology specimen.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".