Burr Hole Endoscopic Mastoidectomy: A Morphometric Cadaveric Study
Bibliographic record
Abstract
Abstract Introduction Traditional open mastoidectomy is performed through a retro-auricular incision to expose the mastoid cortex. Few have addressed the possibility of performing an endoscopic minimally invasive mastoidectomy. Objective Our objective was to test the feasibility of performing an endoscopic mastoidectomy through a 1 cm incision and burr hole. Methods Ten cadaver heads (20 mastoids) were used for this morphometric study. We performed an endoscopic mastoidectomy through a 1 cm burr hole located over the antrum. The goals were to reach predetermined landmarks and maximize the drilling of cancellous mastoid bone. Computed tomography (CT) imaging was acquired at baseline, after endoscopic approach and after traditional open mastoidectomy. The scans were then analyzed with volumetric measurements of each mastoid. Results Endoscopic mastoidectomy facilitated access to most anatomical landmarks. While open mastoidectomy enabled greater extents of mastoidectomy and tegmen exposure, the endoscopic approach exposed 76% of mastoid and 69.9% of the tegmen achievable by the open approach. Additionally, baseline mastoid volume and tegmen surface area positively correlated with the extent of mastoidectomy and tegmen exposure, respectively. Baseline mastoid volume negatively correlated with the percentage of mastoid drilled and tegmen exposed. Conclusion We demonstrated the feasibility of an endoscopic mastoidectomy through a standardized postauricular burr hole. This approach reduces the incision size and the need for soft tissue dissection. Burr hole mastoidectomy is facilitated using angled scopes which are not reliant on 0-degree line-of-sight. Although the endoscopic approach afforded slightly less exposure, the location and burr hole size can be adjusted depending on the clinical indications.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".