Preoperative anesthesiology consult utilization in Ontario – a <scp>population‐based</scp> study
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Physician consultations are a limited resource. Anesthesiologists provide anaesthesia during surgery and procedures, prepare patients for surgery in preoperative clinics, and provide postoperative care. This study sought to evaluate current consultation usage patterns, with an aim to determine possible opportunities for efficiency. METHOD: A retrospective comprehensive population-based cohort study was performed, evaluating all hospitals in the Canadian province of Ontario from 2002 to 2018. The main outcome measures were American Society of Anesthesiologists (ASA) classification of the patients, and whether the patients underwent surgery within 3 months following the anaesthesia consultation. RESULTS: A cohort of 2,023,499 patients, and a total of 2,920,100 preoperative anaesthesia consultations was obtained. The number of consults per year doubled between 2003 (112,983/year) and 2017 (246,427/year), despite a less than 40% increase in practicing Canadian Anesthesiologists over this same timeframe. Each year, an average of 19.3% of the consults (range: 17.7-20.5%) were for patients that did not progress to having surgery. Of those that did have surgery following the anaesthesia consult, 37.2% were ASA Classification I or II. The most common surgical procedures (percent of total) following anaesthesia consult were: Knee arthroplasty (9.5%), hip arthroplasty (5.8%), cataract extraction (4.1%), repair of muscle of chest/abdomen (3.3%), hysterectomy (2.8%), and cholecystectomy (2.7%). CONCLUSIONS: This study reveals data on utilization and trends over time of preoperative anaesthesia consultations. Potential opportunities for optimization were found, including patients who did not proceed to surgery, and healthier patients undergoing low to moderate risk surgery.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".