Advantages of Hyaluronic Acid Fat Graft Myringoplasty Over Fat Graft Myringoplasty
Bibliographic record
Abstract
OBJECTIVES: To compare hyaluronic acid fat graft myringoplasty (HAFGM) with fat graft myringoplasty (FGM) on grade I tympanic membrane perforations (TMPs) (<25% of the tympanic membrane surface) and to assess 12-month postoperative hearing outcomes in a pediatric population. DESIGN: Prospective study. SETTING: Tertiary care pediatric center. PATIENTS Ninety-two children with a TMP were included in the study and were operated on using either the HAFGM (n = 50) or FGM (n = 42) technique. Age at surgery ranged from 4 to 17 years (mean age, 11.52 years). INTERVENTIONS: Hyaluronic acid fat graft myringoplasty is a new technique for TMP repair in a pediatric population and is performed using local anesthesia at the outpatient office. MAIN OUTCOME MEASURES: Postoperative status of the tympanic membrane, hearing improvement, and incidence of complications. RESULTS: Successful closure of the tympanic membrane was achieved in 90% of the HAFGM group and in 57.1% of the FGM group (P = .004). The mean (SD) postoperative air-bone gap in the HAFGM (6.86 [8.52] dB) group was significantly lower than in the FGM (18.32 [13.44] dB) group (P < .002). The mean postoperative follow-up time was 31.5 and 34.7 months for HAFGM and FGM groups, respectively. No difference was observed between children 10 years or younger and children older than 10 years. In the 2 groups, no postoperative complications were observed. The location of the perforation was not found to be a factor indicative of failure. CONCLUSIONS: The success rate of HAFGM is superior to that of FGM alone. Hyaluronic acid fat graft myringoplasty can be performed as an office-based procedure using local anesthesia and requires no hospitalization. Because of its substantial advantages, HAFGM could be considered as the first choice for the reconstruction of a dry TMP in the pediatric population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".