Sanna Classification and Prognosis of Cholesteatoma of the Petrous Part of the Temporal Bone
Bibliographic record
Abstract
OBJECTIVE: To determine how classification of petrous bone cholesteatomas (PBCs) using the 5-point Sanna classification can predict major structural involvement, facial nerve outcomes, hearing outcomes, postoperative complications, and disease recurrence. STUDY DESIGN: Retrospective case series. SETTING: Tertiary referral center in Bergamo, Italy. PATIENTS: Eighty-one sequential patients with radiologic and surgically confirmed diagnoses of PBC treated at a single tertiary referral center during a 20-year period. MAIN OUTCOME MEASURES: Major structural involvement, facial nerve outcomes, hearing outcomes, postoperative cerebrospinal fluid leak, and disease recurrence were evaluated on the basis of Sanna classification. RESULTS: Using the Sanna classification, 70% (57) were supralabyrinthine, 12% (10) infralabyrinthine, 7% (6) infralabyrinthine-apical, 5% (4) apical, and 5% (4) massive. Massive classification was statistically significantly associated with cochlear involvement (p = 0.009) and internal auditory canal involvement (p = 0.02). The infralabyrinthine-apical class was associated with carotid canal involvement (p = 0.03). Facial nerve interruption was observed in 35% of patients and most frequently in the apical group (75%). Neither hearing nor facial nerve outcomes were associated with Sanna classification. House-Brackmann score improved or was maintained postoperatively in 89% of patients. CONCLUSION: The Sanna classification provides anatomic detail on location of PBCs and is predictive of IAC, cochlear, and carotid artery involvement. However, classification systems for this rare condition continue to pose a challenge in being able to accurately predict facial nerve and hearing outcomes in surgical obliteration of PBC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".