Utilization of a preoperative assessment clinic in a tertiary care centre.
Bibliographic record
Abstract
OBJECTIVE: To describe the utilization of a preoperative assessment clinic (PAC) by various surgical divisions, and the types of consultations sought by those divisions. DESIGN: Cross-sectional descriptive study of PAC utilization. SETTING: A large university-affiliated tertiary care centre. PATIENTS: All patients who underwent surgical procedures by selected surgical divisions between July 1, 1996, and Mar. 31, 1998. MEASUREMENTS: The number of patients referred to the centre's PAC, utilization by surgical division, and the types of consultation obtained (general internal medicine, anesthesia, cardiology, intensive care). Adjusted rates of consultations were determined by logistic regression, controlling for age, sex, comorbidity and major versus minor procedure. RESULTS: Of 9603 surgical cases, 5725 (60%) were referred to the PAC. The adjusted rates of PAC utilization ranged from a low of 46% for cardiovascular and thoracic surgery to a high of 72% for general surgery. The adjusted rates of general internal medicine consultations ranged from 5% for oral surgery to 33% for otolaryngology. For anesthesia consultations, the rates ranged from 6% for orthopedics to 39% for general surgery. Increasing age (odds ratio [OR] = 1.14 for 10-year age increments), female sex (OR = 1.23), major surgery (OR = 1.94) and a number of comorbidity variables were significant predictors of PAC referral on multivariable analysis. CONCLUSIONS: PAC utilization varies across surgical divisions and in the types of consultation sought, even when controlling for age, sex, comorbidity and type of procedure. The potential exists for standardized PAC referral guidelines to reduce these variations.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".