Quality of warfarin management following transfer from an anticoagulation clinic to primary care
Bibliographic record
Abstract
BACKGROUND: Advances in alternative oral anticoagulants has reduced use and clinician comfort with warfarin. Our specialty anticoagulation clinic (AC) operates at maximum capacity and must transfer patients to accept new referrals. OBJECTIVES: To compare time within therapeutic range (TTR) during 6 months of AC care versus following transfer to primary care for a minimum of 6 months and to a maximum of 24 months. Secondarily, to compare frequency of INR assessments, proportion of INRs ≤1.5 and > 5, and rates of bleeding and thromboembolic events post-transfer to primary care. METHODS: Mixed retrospective chart review and administrative audit with a before-after study design for patients managed by the University of Alberta's AC for at least 6 months that were transferred to primary care. RESULTS: 177 (27.7 %) patients were included, managed by the AC for 3.4 years (1.3, 7.9). TTR declined during the first 6 months post-transfer with AC care achieving 69.2 % and primary care 64.5 % (p = 0.02) and when compared to the 24-month interval (69.2 % vs 63.4 %; respectively; p = 0.003). A shorter interval between INRs in AC care was observed (28.9 (24.4) vs 34.5 (31.7) days, respectively; p = 0.0004). Similar numbers of critical INRs occurred between groups, whereas more INRs ≤1.5 occurred in primary care (7.3 % vs 4.7 %, respectively; p = 0.0003). Bleeding and thromboembolic event rates were balanced following transfer to primary care with both occurring at 9.4 % per patient year. CONCLUSION: A decline in anticoagulation control after transfer to primary care was observed, which appeared to be driven by a greater proportion of subtherapeutic INRs.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".