Existing Warfarin Therapy in Long-Term Care Facilities Maybe Inadequate.
Bibliographic record
Abstract
Abstract Despite evidence-based guidelines derived from large clinical trials supporting the use of warfarin for stroke prophylaxis, studies in elderly patients have shown that oral anticoagulants are not used optimally. The risk associated with inappropriate use is compounded by the observation that the elderly are at enhanced risk of thromboembolic complications compared with younger atrial fibrillation patients. All patients with atrial fibrillation who do not have a contraindication to warfarin, and who meet inclusion criteria, should be treated with warfarin to achieve a target International Normalized Range (INR) of 2.5 (range 2.0–3.0). INR levels of 2.0–3.0 have been shown to be relatively safe and more efficacious than lower target INR values in all age groups including the elderly. Patients with INR values below this range remain at increased risk of thrombosis, while those with INR values above the given range are at increased risk of bleeding. The primary objective of this study was to determine the achieved intensity of warfarin therapy in a cohort of patients living at long-term care facility. In such facilities optimal anticoagulation should be achievable, since laboratory monitoring, dose adjustment, and compliance can be achieved. In this study, data were collected on physicians’ warfarin prescribing practices as well as INR levels of 108 residents in five long-term care facilities in the Hamilton-Wentworth area over a period of 12 months. In total, 3146 INR values, extending over 28,256 patient-days of monitoring, were analyzed. Indications for warfarin were atrial fibrillation, transient ischemic attack, pulmonary embolus, cardiac valve replacement, myocardial infarction, and deep vein thrombosis. In general, the warfarin dosage was not determined using an established dosing algorithm. Our findings revealed that LTC residents spent approximately 40 percent of the time with INR values below 2.0. We therefore conclude, that the overall quality of anticoagulant therapy in long-term care patients may be inadequate. Our observations suggest that organized dosing algorithms may be of benefit in such settings, however this hypothesis needs to be confirmed in prospective studies. For this purpose we plan to implement a warfarin dosing algorithm in order to determine whether the percentage of time spent within the therapeutic INR range can be improved.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".