The impact of middle ear packing materials on tympanoplasty outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Chronic otitis media and tympanic membrane perforations are common indications for tympanoplasty, where the choice of middle ear packing material critically influences graft survival, hearing restoration, and postoperative complications. OBJECTIVE: To systematically compare the efficacy and safety of different middle ear packing materials-including platelet-rich fibrin (PRF), hyaluronic acid (HA), Gelfoam, fat, and ciprofloxacin-soaked packing-on tympanoplasty outcomes. METHODS: A systematic review and meta-analysis were conducted following PRISMA 2020 guidelines. PubMed, Embase, and Cochrane Library were searched (2019-2025) for RCTs and cohort studies comparing packing materials in tympanoplasty. Data extraction and quality assessment were performed independently by two reviewers using Covidence, applying the Cochrane Risk of Bias 2 tool and Newcastle-Ottawa Scale. Random-effects meta-analysis was conducted using RevMan 5.4. RESULTS: Fifteen studies met inclusion criteria. PRF achieved the highest graft success (93.7%), with HA offering superior hearing improvement (mean ABG closure: 15.2 dB). Both PRF and HA were associated with significantly fewer complications compared to Gelfoam or fat. Bacterial cellulose showed promise for small perforations. Substantial heterogeneity was observed in studies using traditional materials. CONCLUSION: PRF and HA are superior to traditional packing materials in tympanoplasty, optimising graft survival and functional outcomes. Evidence-based selection of biologically active materials is recommended to improve patient care, with further research needed to standardise protocols and confirm long-term benefits.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".