Treatment Effectiveness for Symptoms of Patulous Eustachian Tube
Bibliographic record
Abstract
OBJECTIVES: To determine the effectiveness of currently available medical and surgical interventions for treating symptoms of Patulous Eustachian Tube (PET). DATA SOURCES: A comprehensive search of MEDLINE (January 1948 to July 8, 2015), EMBASE (January 1974 to July 8, 2015), gray literature, hand searches, and cross-reference checking. STUDY SELECTION: Original published reports evaluating an intervention to treat the symptoms of patulous eustachian tube in patients 18 years and older. DATA EXTRACTION: Quality-of-case reviews were assessed with the National Institute of Health (NIH) Quality Assessment Tool for Case Series Studies. DATA SYNTHESIS: The search strategy identified 1,104 unique titles; 39 articles with 533 patients are included. The available evidence consists of small case series and case reports. The most common medical treatment was nasal instillation of normal saline. Surgical treatments were categorized as mass loading of the tympanic membrane, eustachian tube plugging, and manipulation of eustachian tube musculature. CONCLUSIONS: The available evidence for management of patients with PET is poor in quality and consists predominantly of small case series. Further research is needed to determine the comparative efficacy of the current treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".