The behavior of mastoidectomy cavities following modified radical mastoidectomy
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: An analysis of the frequency and intensity of postoperative aftercare required for modified radical mastoidectomy (MRM) and patterns of healing in the postoperative period. STUDY DESIGN: A retrospective review of all primary modified radical mastoidectomies carried out for cholesteatoma under the care of the senior author between the years of 2004 and 2009 with minimum follow-up of 2 years. METHODS: The time and number of interventions required to achieve a stable and dry mastoid cavity were collected. Cross-sectional and longitudinal analysis of the behavior of the cavities was carried out. RESULTS: Overall, 73 cases (71 patients) were identified. Patients were followed up for a median of 45.7 months (interquartile range, 31.8-70.5). After initial debridement, most cavities settled rapidly, but this was not always predictable, with a large proportion requiring further clinical intervention after the cavity was stable, sometimes for prolonged periods of time. At the time of analysis, 73% had achieved a stable cavity, 17 (23%) still required attention (nine for wax removal and eight for debridement); two were lost to follow-up. No revision surgeries were required. At 6 months, 36% of cavities were settled, 42% at 1 year, 53% at 18 months, and 62% at 2 years. After two standard postoperative visits, a total of 632 visits were made by these patients. CONCLUSIONS: Following MRM, the majority of patients achieve a dry, self-cleaning mastoid cavity. This might require periods of intense care interspersed with periods of quiescence. These results allow the benefits of this procedure to be put in the context of the entire patient journey.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".