Office-Based Middle Ear Surgery Under Local Anesthesia: A Contemporary Review
Bibliographic record
Abstract
Middle ear surgery is a daily routine procedure in otolaryngology. Pain control, along with a bloodless and non-moving operative field during the surgery, is crucial for achieving better surgical outcomes and can be achieved easily under general anesthesia (GA). Nevertheless, local anesthesia (LA), especially if done as office-based surgery, is an ideal alternative to GA in certain scenarios. Despite the well-established nature of LA for middle ear surgery, only a small percentage of otolaryngologists choose to use it, and few publications in the literature address this topic. This article reviews the literature for the feasibility of performing middle ear surgeries in an office base setting. A scoping review of the literature was conducted on middle ear surgery under LA in an office-based setting, focusing on feasibility, advantages, surgical techniques, limitations, and outcomes, to provide a concise guide for its implementation. LA will avoid the rare but serious complications of GA, and there are cost savings of approximately 50% in LA, which is crucial for the sustainability of healthcare systems. It is an ideal alternative to GA in certain scenarios, including decreased access to operative rooms, staff shortage or contraindications for GA. Also, surgeons can appreciate hearing improvement and the need for prosthesis adjustment with instant feedback during the surgery or prevent significant complications such as a dead ear under LA. Office-based surgery for middle ear under LA is feasible and the present work offers a brief guide for beginners with some tips and tricks to ensure optimal patient care and outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".