Mastoidectomy and mastoid obliteration with autologous bone graft: A quality of life study
Bibliographic record
Abstract
BACKGROUND: A mastoid cavity resulting from a canal wall down mastoidectomy can result in major morbidity for patients due to chronic otorrhea and infection, difficulty with hearing aids and vertigo with temperature changes. Mastoid obliteration with reconstruction of the bony external ear canal recreates the normal anatomy to avoid such morbidity. Few have the studied the quality of life benefit that this procedure confers. METHODS: This retrospective observational study was conducted to determine if mastoid obliteration with autologous cranial bone graft following mastoidectomy improves quality of life (QOL). Patients with cholesteatoma who had mastoidectomy with primary or secondary mastoid obliteration by a tertiary otologist were surveyed using the validated Glasgow Benefit Inventory (GBI), our primary outcome measure. RESULTS: Fifty-eight patients were interviewed. Forty-six were primary obliteration after canal wall down mastoidectomy of a primary cholesteatoma. Twelve were secondary obliteration of an existing canal wall down mastoid cavity. Overall GBI scores were improved, with average scores of 22. Average general subscale scores were 23, physical health scores were 25, and social health scores were 22. The primary obliteration group had average scores of 19, general subscale scores of 20, physical health scores of 21, and social health scores of 22. Those with secondary obliteration scored higher, with average scores of 31, general subscale scores of 34, physical health scores of 39, and social health scores of 25. CONCLUSION: This study shows that mastoidectomy with obliteration using autologous cranial bone graft offers a significant QOL benefit. The GBI scores compare favourably with other otorhinolaryngology procedures. Secondary obliterations after revision mastoidectomy scored much higher than primary obliterations. This is currently the only QOL study comparing these two patient groups.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".