Double-Layer Tympanic Membrane Graft in Type I Tympanoplasty
Bibliographic record
Abstract
Background: Some discussion remains among otologists regarding the best grafts for tympanic membrane closure. It is unclear whether double-layer grafts are superior to single-layer and whether single-layer cartilage is superior to fascia or perichondrium alone. The objective of the current study was to examine the relative efficacy of single-layer versus double-layer tympanic membrane grafting techniques. Materials and Methods: A retrospective review of the medical records was used to address the objective of the study. Patients operated on in an over/under technique by the same surgeon underwent single-layer perichondrium or single-layer perichondrium with a cartilage island, or a double-layer of perichondrium combined with periochondrium with a cartilage island. The outcomes assessed were tympanic membrane reperforation and hearing improvement. Results: A total of 135/177 (76%) perichondrium grafts had no reperforation, and 43/55 (78%) perichondrium with cartilage island grafts had no reperforation; 352/390 (90%) of the double-layer closures had no reperforation. There was no statistically significant difference in reperforation rates between the 2 single-layer techniques ( P = .926). The difference in reperforation rates after the double-layer closure versus the perichondrium single-layer closure was statistically significant ( P = .001), as was the difference in reperforation rates after the double-layer closure versus the cartilage island single-layer closure ( P = .02). All 3 groups showed statistically significant hearing improvement postoperatively ( P < .0001). Preoperative hearing levels ( P = .179), postoperative hearing ( P = .857), and decibels of hearing improvement ( P = .356) were the same for all 3 groups. Conclusion: Double-layer closure gives lower tympanic membrane reperforation rates than does single-layer closure, as well as similar hearing outcomes.
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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 | 0.002 |
| 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) | 0.001 | 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".