Successful use of a patient specific 3D-printed biomodel as surgical guide for excision of juvenile nasopharyngeal angiofibroma extending to skull base: A case report
Bibliographic record
Abstract
Background: 3-Dimensional (3D) printing has proven its role in various fields. Recently, 3D printing has also been introduced in the otolaryngology domain. The nasopharynx, paranasal sinuses, and the anterior skull base have a complex anatomy. Critical structures must be delicately protected and preserved during a surgical procedure. It is, therefore, very important for the surgeon to have an excellent spatial understanding of the complex surgical field that is being traversed. Case Description: Our case is of a 19-year-old male with a 2-month history of recurrent epistaxis, nasal blockage, and headache. Based on the computed tomography scan and the clinical presentation, the patient was diagnosed with juvenile nasopharyngeal angiofibroma. The patient underwent angioembolization of the tumor followed by endoscopic surgical resection. The patient remained stable postoperatively and demonstrated a good recovery in the follow-up visit with no signs of cranial deficits. This case report highlights the use of a patient-specific 3D-printed biomodel to visualize this rare tumor of the nasopharynx. The benefits of using the model in surgical planning, patient education, and resident training are reported. We found that the ability to visualize the tumor on a tangible model, viewing its actual size in relation to the adjacent anatomy and all the structures associated with it, greatly enhances the surgeon's capacity to tackle such a difficult tumor endoscopically. Conclusion: Incorporating 3D-printed biomodels in surgical practice should result in improved outcomes for the patients.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".