Hyaluronic Acid Fat Graft Myringoplasty<subtitle>An Office-Based Technique Adapted to Children</subtitle>
Bibliographic record
Abstract
OBJECTIVES: To evaluate hyaluronic acid fat graft myringoplasty (HAFGM) for different tympanic membrane perforation (TMP) sizes and to compare its success rate with that of the underlay and overlay techniques. DESIGN: Prospective study. SETTING: Tertiary care pediatric center. PATIENTS: Two hundred eight children aged 4 to 16 years (mean age, 11.84 years) with TMPs. INTERVENTIONS: The HAFGM is a new technique for TMP repair in an outpatient pediatric population using local anesthesia. All the patients in groups 1 (underlay) and 2 (overlay) were operated on using general anesthesia, whereas group 3 (HAFGM) was operated on at the outpatient office using local anesthesia. MAIN OUTCOME MEASURES: Postoperative status of the eardrum, hearing improvement, and incidence of complications. RESULTS: Patients with TMP were divided into 3 groups: group 1 had 75 patients; group 2, 65; and group 3, 73. The global success rate was 87% in group 3, with no difference with the remaining 2 groups. Successful closure of different TMP sizes was the same for the 3 groups. Postoperatively, air-bone gap improvement was better for group 3. No bone conduction threshold worsening was noted. The mean duration of the operative procedure was 65, 74, and 18 minutes for groups 1, 2, and 3, respectively (P = .02). Mean postoperative follow-up was 20.7, 17.5, and 14.6 months for groups 1, 2, and 3, respectively. Identification of the anterior perforation rim is mandatory to perform HAFGM. CONCLUSIONS: The HAFGM did not require hospitalization for pediatric patients. It had the advantage of being feasible in children using local anesthesia. Its success rate was comparable with that of conventional techniques.
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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) | 0.001 | 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".