Resection and Reconstruction of a Giant Paranasal Osteoma in a Pediatric Patient
Bibliographic record
Abstract
Study Design Case Report. Objective Paranasal osteomas are rare, benign bony tumors of the craniofacial skeleton and most are treated endoscopically without the need for reconstruction. In rare cases, open traditional craniofacial approaches are required with a paucity of literature describing extirpative maneuvers in this setting. We present a unique case of a rapidly growing, paranasal bony tumor in a pediatric patient and describe our surgical technique for resection and reconstruction. Methods A 13-year old male presented with eight months of progressive, painless right periorbital swelling and proptosis. Trans-nasal biopsy confirmed an osteoma of the right ethmoid sinus and endoscopic resection was recommended. Due to interval tumor growth, there was worsening visual acuity that precluded a minimally invasive approach. Pre-operative and post-operative 3D computed tomography (CT) scans were completed, and a 3D printed skull model was produced to aid in surgical planning. A multi-disciplinary team of Otolaryngology, Neurosurgery, and Plastic Surgery was employed. The majority of the extirpation was performed with a highspeed carbide burr. Results Post-operative course was unremarkable and CT scan demonstrated a complete resection. Pathology confirmed the diagnosis of an osteoma. Six-month follow-up demonstrated normal ophthalmologic function and visual acuity with excellent aesthetic results. Conclusions This is a rare, complex presentation of a paranasal osteoma that was successfully managed with an open, multi-disciplinary approach. We highlight pearls gleaned from this extirpative and reconstructive technique. Due to the tumor density and location, a multidisciplinary team, Stealth navigation, an open, intra- and extra-cranial approach and use of a highspeed carbide burr were essential.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".