A Pharmacist-Led Practice to Improve Perioperative Glycemic Control in Elective Surgery
Bibliographic record
Abstract
The South Health Campus (SHC) is a tertiary care hospital located in Calgary, Alberta, Canada. It has ~275 inpatient beds and multiple outpatient clinics serving Calgary and Southern Alberta. SHC has team-based pharmacy services coverage, through which the same pharmacist team will cover an inpatient unit and that unit’s corresponding outpatient clinic. For example, the inpatient cardiology pharmacist team also works in the hospital’s outpatient cardiology clinic and the inpatient neurology pharmacist team also works in the neurology outpatient clinic. Surgical pharmacist team members split their time between the inpatient surgical unit and the outpatient pre-admission clinic (PAC). In the PAC, people with planned surgery are seen by a team of internists, anesthesiologists, nurses, and pharmacists if they have complex medical conditions (e.g., coronary artery disease, inflammatory bowel disease on biologic drug therapy, active cancer, or an indication for anticoagulation therapy). This pre-surgery process is to ensure that patients are medically optimized before surgery and to provide recommendations for perioperative care to the surgeons. In the PAC, patients with diabetes are given standardized recommendations based on internal guidelines for which oral antidiabetic medications they should take or hold on the morning of surgery and, if applicable, how to adjust their long-acting insulin the day of or night before 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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".