Incidence Rate and Determinants of Recurrent Cholesteatoma Following Surgical Management: A Systematic Review, Subgroup, and Meta-Regression Analysis
Bibliographic record
Abstract
Background/Objectives: Cholesteatoma is a destructive middle ear pathology that can cause chronic infection, ossicular erosion, and hearing loss. While surgical excision is the standard treatment, recurrence remains a major clinical challenge, and comprehensive data on long-term outcomes are limited. This meta-analysis evaluated cholesteatoma recurrence rates following surgery, identified clinical and surgical predictors of recurrence, and assessed trends across follow-up durations, techniques, and patient demographics. Methods: We searched PubMed, Scopus, Web of Science, CENTRAL, and Google Scholar for relevant studies (CRD42024550351). Studies reporting postoperative recurrence were included. Data on demographics, surgical approach, cholesteatoma type, and outcomes were extracted. Risk of bias was assessed using the Newcastle–Ottawa Scale. Pooled recurrence rates were calculated using random-effects models, and subgroup and meta-regression analyses were performed to identify predictors. Results: Eighty-four studies comprising 12,819 patients were included. The cholesteatoma recurrence rate showed geographic variability. Recurrence was higher in children (13%) than adults (10%), and in acquired (12%) versus congenital (7%) cholesteatoma. Advanced-stage disease, left-sided lesions, and revision surgeries increased recurrence risk. Canal wall down had lower recurrence (7%) than canal wall up techniques (16%). Adjuncts such as mastoid obliteration, ossicular reconstruction, and planned second-look surgeries reduced recurrence. Cumulative recurrence reached 39% at 15 years and 33% at 25 years. Meta-regression identified age, staged procedures, and second-look surgeries as independent predictors. Conclusions: Cholesteatoma recurrence is influenced by age, surgical approach, and disease severity. CWD procedures and comprehensive surgical planning reduce recurrence risk. Long-term follow-up and standardized outcome definitions are essential to improve monitoring and disease control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.043 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".