Preoperative Medication Management, Compliance and Adverse Events in Adult Patients Undergoing Elective Surgery: A Historical Chart Review
Bibliographic record
Abstract
Purpose: Medication compliance for chronic medications has been well studied, but there is a gap in the literature regarding compliance within the perioperative period. Our objective was to determine the incidence of patient non-compliance with preoperative medication instructions for adult non-emergent surgery. Additional objectives were to identify predictors of compliance, describe medication instructions by drug type, and explore the impact of non-compliance. Patients and Methods: This historical chart review evaluated preoperative compliance to medication instructions in 393 adults undergoing non-emergent surgeries at Hamilton Health Sciences between May 1, 2012, and April 30, 2013. Seven patient factors (age; sex; American Society of Anesthesiologists class; number of medications; type of surgery; time between preoperative appointment to surgery; the individual collecting the medication list) were evaluated as potential predictors of non-compliance and analyzed using logistic regression analysis. Consequences of non-compliance were assessed by impact on intraoperative blood pressure, blood glucose level, drop in hemoglobin, bronchospasm, and case delays. Results: One hundred forty-six (37.2%) patients were non-compliant with one or more medication reconciliation instructions provided by the anesthesiologist. No significant associations were observed for any patient risk factors and non-compliance. Non-compliance was not associated with any clinically significant consequences. Conclusions: Our study shows that 37.15% of adult patients undergoing non-emergent surgery were non-compliant with medication instructions, although patients did not receive any written instructions for 46% of their medications. We did not identify any predictive patient factors or adverse outcomes associated with non-compliance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".