Potentially Unintended Discontinuation of Long-term Medication Use After Elective Surgical Procedures
Bibliographic record
Abstract
BACKGROUND: Transitions between health care settings represent vulnerable periods for medical error. Discontinuation of long-term medication use may occur during discharge from the hospital to the community. METHODS: We performed a population-based, cohort study using administrative records from Ontario, Canada, between April 1, 1997, and September 30, 2002. We studied all residents 66 years and older with continuous use of warfarin, 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins), or beta-blocker ophthalmic drops for 1 or more years. Those who had an overnight hospitalization for selected elective surgical procedures were compared with 2 control groups: one that had an ambulatory procedure and one that had no procedures. All groups were assessed for the outcome of failure to renew the prescription within 6 months. RESULTS: Rates of drug treatment discontinuation after overnight hospitalizations, after ambulatory procedures, and after no procedures were 11.4%, 7.5%, and 4.8%, respectively, in the warfarin group; 4.0%, 3.9%, and 3.9%, respectively, in the statin group; and 8.4%, 8.9%, and 7.9%, respectively, in the ophthalmic drops group. The adjusted odds ratio (OR) was 2.6 (95% confidence interval [CI], 2.0-3.4) for discontinuation of warfarin therapy after overnight hospitalizations and 1.6 (95% CI, 1.4-1.7) after ambulatory procedures. In contrast, there was no increased risk of discontinuing treatment with either statins (OR for overnight hospitalization, 1.0 [95% CI, 0.9-1.2]; OR for ambulatory procedure, 1.0 [95% CI 1.0-1.1]) or ophthalmic drops (OR for overnight hospitalization, 1.0 [95% CI, 0.8-1.5]; OR for ambulatory procedure, 1.1 [95% CI, 1.0-1.2]). CONCLUSIONS: Patients prescribed long-term therapy with warfarin were at risk for potentially unintended medication discontinuation after elective procedures. Patients prescribed statins or beta-blocker ophthalmic drops were not at increased risk.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".