[Parapharyngeal tumors: management and postoperative results].
Bibliographic record
Abstract
OBJECTIVES: Parapharyngeal space tumours are rare and mostly benign, and their access is difficult. The aim of the study was to define the optimal treatment of these tumours. MATERIAL AND METHODS: Clinical retrospective study, with histological, treatment, prognosis analysis of the parapharyngeal space tumours occurred between 1994 and 2012 and the surgical approach considerations. RESULTS: 35 patients were included with a male sex ratio predominance (51.43%, p = ns). The mean age of diagnosis was 49 years. The majority of tumours were benign (75.76%, p < 0.001). 54.5% originated from salivary gland with a majority of pleomorphic adenomas (42.42%). The second most frequent aetiology found was schwannoma (21.21%). 2 patients refused the treatment. Accidentally discovered were frequent (28.57%). In the symptomatic forms, dysphagia dominated (25.71%). Oropharyngeal (20%) and/or cervical (17.14%) mass were rare. A CT scan and a MR imaging were performed in 82.86% and 71.43% of the cases. A fine needle aspiration cytology was achieved in 10 cases with a positive predictive value of 60%. The cervical submandibular trans digastric approach was the most accomplished (40%). Other approaches were also used: Trans oral (25.71%), parotidectomy (14.71%), combined trans oral and cervical or parotidectomy (14.71%) and the cervical approached associated to a mandibulotomy (5.71%). A complementary treatment was proposed in 85.7% of the cases of malignant tumours. Post-operative complications were dominated by Horner's syndrome (n = 3), a temporary facial palsy (n = 5), definitive (n = 1), a velar insufficiency (n = 1), persistent pain (n = 6). CONCLUSIONS: The presence of 25% of malignant tumours and 40% of pleomorphic adenomas require removal of parapharyngeal tumours, even if non symptomatic.
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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 | 0.001 |
| 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) | 0.003 | 0.001 |
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".