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Development of an Appropriate List of Surgical Procedures of a Specified Maximum Anesthetic Complexity to Be Performed at a New Ambulatory Surgery Facility

2002· article· en· W2141867061 on OpenAlexaff
Franklin Dexter, Alex Macario, Donald H. Penning, Patricia Chung

Bibliographic record

VenueAnesthesia & Analgesia · 2002
Typearticle
Languageen
FieldMedicine
TopicNausea and vomiting management
Canadian institutionsSunnybrook Health Science CentreHealth Sciences Centre
Fundersnot available
KeywordsMedicineAmbulatorySurgical proceduresTask (project management)Operations managementMedical emergencySurgery

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.087
GPT teacher head0.277
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations36
Published2002
Admission routes1
Has abstractyes

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