Huge Lobulated Juvenile Angiofibroma: Sites of Extension and Selection of Procedures for Management
Bibliographic record
Abstract
OBJECTIVE: To review and report our experiences in the management of huge lobulated juvenile angiofibroma (JNA). MATERIALS AND METHODS: From 1980 to 2001, 11 cases were diagnosed and treated in our department. Their clinical manifestations and the results of treatment were analyzed and evaluated, with particular attention to the anatomic sites of extension and the selection of choices for appropriate treatment. RESULTS: Owing to extranasopharyngeal extension, 6 to 12 different anatomic sites were occupied by each tumour, including the cheek (9 tumours) and middle cranial fossa (3 tumours). To remove these tumours, a transantral approach via midfacial degloving was usually used, with or without another incision and approach combined. When the middle cranial fossa is extensively involved by a tumour lobe, it should first be exposed and managed through a transtemporal-extradural or intradural approach. The internal carotid artery and cavernous sinus can be controlled distally. JNAs in 9 cases were resected completely without recurrence after following up for 1 to 22 years. The other 2 JNAs with large intracranial extension and serious intraoperative bleeding were removed subtotally. CONCLUSION: A huge JNA might occupy multiple anatomic sites, and the transantral approach via midfacial degloving is recommended as the best choice for its removal, with another incision and approach combined, if necessary.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".