Cholesteatoma in three dimensions: a teaching tool and an aid to improved pre-operative consent
Bibliographic record
Abstract
BACKGROUND: Otological surgeons face two recurring challenges. Firstly, we must foster an appreciation of the complex, three-dimensional anatomy of the temporal bone in order to enable our trainees to operate safely and independently. Secondly, we must explain to our patients the necessity for surgery which carries the potential for serious complication. METHODS: Amira software was applied to pre-operative computed tomography images of temporal bones with cholesteatoma, to create three-dimensional computer images. Normal structures and cholesteatoma were displayed in a user-friendly, interactive format, allowing both trainee and patient to visualise disease and important structures within the temporal bone. RESULTS: Three cases, and their three-dimensional computer models are presented. Zoom, rotation and transparency functions complemented the three-dimensional effect. CONCLUSION: These three-dimensional models provided a useful adjunct to cadaveric temporal bone dissection and surgical experience for our residents' teaching programme. Also, patients with cholesteatoma reported a better understanding of their pre-operative condition when the models were used during the consenting process.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".