Bibliographic record
Abstract
OBJECTIVE: To describe the author's method of managing occlusive exostosis of the external auditory canal. STUDY DESIGN: Retrospective chart review. SETTING: Tertiary referral ambulatory otology clinic. PATIENTS: A case series of patients treated sequentially by the author over 15 years, all of whom had occlusive external auditory canal exostoses that could not be treated by medical management. INTERVENTION: Permeatal surgical removal of the anterior exostosis only. MAIN OUTCOME MEASURE: Surgical relief of occlusive external auditory canal disease by restoration of hearing and absence of infection with persistence of an external auditory canal and no symptoms of recurrence. RESULTS: A total of 8 men were treated by anterior exostosis removal. Follow-up continued on these patients for a period of 5 to 15 years after the operation, and none showed any evidence of recurrence or tendency to narrowing of the deep ear canal. One patient incurred a tympanic membrane perforation at escostosis surgery that was repaired during the operation. CONCLUSION: Anterior exostosis removal by a permeatal route is a safe, rapid, and effective method of relieving patients of occlusive external auditory canal exostosis. By leaving the posterior exostosis intact, patients are not put at risk for injury to the facial nerve, chorda tympani nerve, or ossicles. When the deep ear canal is drilled blind, there are no landmarks to indicate the true path of the external canal.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".