Admission Rates Following Day-Case Major Otologic Surgery: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To assess whether day-case major otologic ear surgery is a safe and feasible alternative to inpatient surgery, while maintaining equal complication rates, and to identify causes of admission after day-case surgery. DATA SOURCES: PubMed, Embase, and Cochrane. REVIEW METHODS: A systematic search was conducted. Studies reporting original data on the effect of day-case ear surgery on admission rate, patient satisfaction, and/or postoperative complications were included. Risk of bias of the selected articles was assessed using criteria based on the Cochrane Collaboration's tool for assessing risk of bias. RESULTS: A total of 1,734 unique studies were retrieved of which 35 articles discussing 34 studies were eligible for data extraction. The admission rates ranged from 0% to 88% following day-case endaural surgery, 0% to 13% following day-case stapes surgery, 0% to 82% following day-case mastoid surgery, and 0% to 15% following day-case cochlear implant surgery. Patient and parent satisfaction regarding day-case surgery ranged from 67% to 99%. Five studies comparing day-case and inpatient otologic surgery showed no difference in hearing outcome, postoperative complications, or patient satisfaction. CONCLUSION: The highest pooled admission rate was seen following day-case mastoid surgery. Studies comparing day-case and inpatient care suggest hearing results and postoperative complication rates in day-case otologic surgery are similar to inpatient otologic surgery in both children and adults. Therefore, day-case major otologic surgery seems to be a safe and feasible alternative to inpatient surgery for both children and adults.
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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 | 0.009 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".