Management of Parapharyngeal Space Tumors: Clinical Experience with a Large Sample and Review of the Literature
Bibliographic record
Abstract
Parapharyngeal space (PPS) tumors are rare, and they account for 0.5-1.5% of all head and neck tumors. This study summarized the findings of large-sample clinical studies of PPS tumors and reported the clinical work-up and management of 177 cases of PPS tumors at our center. This retrospective study included patients treated for PPS tumors between 2005 and 2020 at our center. The basic characteristics, symptoms, surgical approach, complications, and recurrence rates were analyzed. A total of 99 male and 78 female patients, with a mean age of 48.3 ± 15.1 years, were enrolled in this study. The most common symptoms were external or intraoral masses (114 patients, 64%). Surgical management leveraging, a cervical approach, was used for 131 cases (74%). The tumors were benign for 92% (160 cases), with pleomorphic adenoma being the most common (88 cases, 50%). Surgical complications were reported for 31 cases (18%); facial and vocal cord paralyses were the most common. Three cases of recurrence were observed during the follow-up. PPS tumors are rare and present with atypical clinical manifestations. The current study, which involved cases in a large single center, demonstrates the importance of surgical interventions for PPS tumors. The use of endoscopic techniques has further expanded the scope of traditional surgical approaches and demonstrated its advantages in selected cases.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".