Hyaluronic Acid Fat Graft Myringoplasty Versus Autologous Platelet Rich Plasma
Bibliographic record
Abstract
BACKGROUND: Hyaluronic acid fat graft myringoplasty (HAFGM) is an office-based technique for tympanic membrane perforation (TMP) treatment. It is simple, inexpensive, and performed under local anesthesia at the outpatient office department. We aimed to compare HAFGM technique to a recently described topical use of autologous platelet rich plasma myringoplasty (PRPM) in the repair of TMP. We also aimed to assess the hearing level improvement postoperatively. METHODS: We conducted a prospective study in an adult tertiary care center between January 2015 and January 2016. Adult patients presenting with simple TMP were operated randomly using either HAFGM or PRPM under local anesthesia in an office-based setting. Perforations were classified into four grades. Success was considered when complete closure is achieved. Audiometric parameters were evaluated pre- and postoperatively. RESULTS: We included 27 patients, of whom 16 were operated with HAFGM and 11 were operated with PRPM. Complete closure was achieved in 81.2% and 18.1%, respectively. Postoperatively, no worsening of bone conduction threshold was noted. The study was abandoned due to the low success rate in patients with PRPM. The pure tone audiometry was improved postoperatively in patients with closed tympanic membrane. CONCLUSIONS: The study was aborted because of the unsatisfactory obtained results using PRPM. It confirms once again the beneficial effect of hyaluronic acid in the healing process when added to fat graft myringoplasty. Furthermore, it requires no hospitalization.
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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 | 0.001 |
| 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".