Quality of anticoagulation and use of warfarin-interacting medications in long-term care: A chart review
Bibliographic record
Abstract
BACKGROUND: Maintenance of therapeutic International Normalized Ratio (INR) in the community is generally poor. The supervised environment in long-term care facilities may represent a more ideal setting for warfarin therapy since laboratory monitoring, compliance, dose adjustment, and interacting medications can all be monitored and controlled. The objectives of this study were to determine how effectively warfarin was administered to a cohort of residents in long-term care facilities, to identify the proportion of residents prescribed warfarin-interacting drugs and to ascertain factors associated with poor INR control. METHODS: A chart review of 105 residents receiving warfarin therapy in five long-term care facilities in Hamilton, Ontario was performed. Data were collected on INR levels, warfarin prescribing and monitoring practices, and use of interacting medications. RESULTS: Over a 12 month period (28,555 resident-days, 78.2 resident years) 3065 INR values were available. Residents were within, below and above the therapeutic range 54%, 35% and 11% of the time, respectively. Seventy-nine percent of residents were prescribed at least one warfarin-interacting medication during the period in review. Residents receiving interacting medications spent less time in the therapeutic range (53.0% vs. 58.2%, OR = 0.93, 95% confidence interval 0.88 to 0.97, P = 0.002). Adequacy of anticoagulation varied significantly between physicians (time in therapeutic range 45.9 to 63.9%). CONCLUSION: In this group of long-term care residents, warfarin control was suboptimal. Both prescriber and co-prescription of interacting medications were associated with poorer INR control. Future studies should seek strategies to improve prescriber skill and decrease use of interacting medications.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".