Tympanic Membrane Manipulation to Treat Symptoms of Patulous Eustachian Tube
Bibliographic record
Abstract
OBJECTIVE: Patulous eustachian tube (PET) can have a significant negative impact on a patient's quality of life. Previous work has demonstrated that temporarily mass loading and stiffening the tympanic membrane significantly reduces these symptoms. This study examined KTP laser myringoplasty (LM) and cartilage tympanoplasty (CT) as a means to manipulate the tympanic membrane to alleviate PET symptoms. STUDY DESIGN: Retrospective case review. SETTING: Academic tertiary care referral hospital. PATIENTS: Patients (n = 20) were identified from the senior authors' (M.B.) specialty eustachian tube disorders clinic. Patients met previously established diagnostic criteria for PET. All patients had a clinically apparent flaccid segment of the eardrum and had symptom improvement after simple mass loading of their eardrum in the clinic. INTERVENTIONS: Patients in this study received either KTP LM (10 patients, 15 ears) or CT (10 patients, 11 ears) to treat their flaccid eardrum segment in an attempt to alleviate PET symptoms. MAIN OUTCOME MEASURES: Preoperative and postoperative questionnaire scores and tympanometry measurements were compared. RESULTS: Patients undergoing CT for PET had a significant reduction in their symptoms of autophony (p ≤ 0.001), conducted breath sounds (p = 0.001), and aural fullness (p = 0.009). KTP LM did not significantly reduce symptoms. CONCLUSION: Cartilage tympanoplasty provides a safe and accessible surgical option for the treatment of PET and significantly reduces the symptoms of autophony, conducted breath sounds, and aural fullness. Further studies are needed to investigate whether addressing PET symptoms simultaneously from both the tympanic membrane and the eustachian tube orifice can improve patient symptoms even further.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".