Local Versus General Anesthesia in Pediatric Otoplasty: A Cost and Efficiency Analysis
Bibliographic record
Abstract
OBJECTIVE: Quantify the cost benefits of otoplasty under local as opposed to general anaesthesia. DESIGN: A cost analysis of all components of otoplasty surgery under local anaesthesia (LA) in a minor operating room (OR) and general anaesthesia in a main OR was performed. SETTING: Our institution, compared to provincial/federal data, with costs converted into 2022 Canadian dollars. PATIENTS, PARTICIPANTS: Patients undergoing otoplasty under LA in the last year. INTERVENTIONS: An efficiency analysis was performed by means of an opportunity cost, and the cost of failure was added to the overall LA costs. MAIN OUTCOME MEASURE: Expenses for infrastructure, surgical and anaesthetic material, salaries, and personnel costs were derived from the literature, our hospital OR catalog and federal/provincial salary data, respectively. The cost of failure to tolerate local anaesthesia for such cases was also tabulated. RESULTS: The true cost of LA otoplasty was computed as the absolute cost ($611.73) added to the cost of failure ($10.80), resulting in a total of $622.53/procedure. The true cost of GA otoplasty was calculated as the absolute cost ($2033.05) added to the opportunity cost ($1108.94), representing 3141.99$/procedure. The total savings when performing LA otoplasty to GA otoplasty are thus 2519.44$/case, with 1 GA otoplasty costing 5.05 LA otoplasties. CONCLUSION: Otoplasty under local anaesthesia offers significant cost savings when compared with the same procedure under general anaesthesia. Economic considerations must be given particular attention given the elective nature of this procedure, which is often publicly funded.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".