Development of an Appropriate List of Surgical Procedures of a Specified Maximum Anesthetic Complexity to Be Performed at a New Ambulatory Surgery Facility
Bibliographic record
Abstract
UNLABELLED: A common but difficult task for a hospital when it decides to open a freestanding ambulatory surgery facility is how to decide which surgical procedures should be done at the new facility. This is necessary in order to determine how many operating rooms to plan for the new facility and which ancillary services are needed on-site. In this case study, we describe a novel methodology that we used to develop a comprehensive list of procedures to be done at a new ambulatory facility. The level of anesthetic complexity of a procedure was defined by its number of ASA Relative Value Guide basic units. Broad categories of procedures (e.g., eye surgery) were defined according to the International Classification of Diseases, Ninth Revision, Clinical Modification. We identified 22 categories that are of a type that every procedure in the category has no more than seven basic units. In addition, by analyzing all procedures that the hospital being studied actually performed on an ambulatory basis, we identified six other categories of procedures that were of a type that all procedures eligible for surgery at the new facility had seven or fewer basic units. IMPLICATIONS: We describe a novel method to develop a comprehensive list of procedures that have a prespecified maximum level of anesthetic complexity to be performed at a new ambulatory surgery facility.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".