Assessment of Oral Anticoagulant Use in Residents of Long-Term Care Homes: Evidence for Contemporary Suboptimal Use
Bibliographic record
Abstract
OBJECTIVE: To describe the quality of warfarin use in residents of long-term care facilities and investigate potential predictors oral anticoagulant use. DESIGN: Retrospective chart review (August 2013 to September 2014). SETTING: Thirteen long-term care (LTC) and assisted living facilities (ALF). PARTICIPANTS: Residents from LTC or ALF settings who ( a) received warfarin or direct-acting oral anticoagulants (DOACs) and ( b) residents with a valid indication for oral anticoagulants such as atrial fibrillation, venous thromboembolism, but were not receiving these drugs. PRIMARY OUTCOME: Time in therapeutic international normalized ratio (INR) range (TTR). RESULTS: A total of 563 residents (70% female) with an average age of 85 years were identified. Participants had an average of 7.5 comorbidities and 9 medications. A total of 391 (69%) residents with indications for OACs were receiving such medications. Indications were atrial fibrillation (63%), venous or pulmonary embolism (16%), cardiac valves (0.4%); 26% did not have documented indications. Warfarin and DOACs were prescribed for 213 (38%) and 178 (32%) respectively, and 172 (31%) received no OACs The TTR ranged from 56%-75% (mean 63%). The frequency of INR determinations ranged from every 7 to 20 days, (mean 13 days) with no apparent relationship between frequency of testing and TTR. CONCLUSION: The TTR was higher (63.8%) than literature average (50%), but remains suboptimal given expected benefits of TTRs >75% versus TTRs circa 60%. Documentation of indications for OACs needs improvement, and it is possible that OACs are underused. Further work is necessary to understand how OAC use may be optimized in these facilities.
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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 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".