Anticoagulation management in remote primary care.
Bibliographic record
Abstract
OBJECTIVE: To examine anticoagulation management at the Bella Coola Medical Clinic in British Columbia. DESIGN: Charts of all patients in the Bella Coola Valley receiving warfarin were assessed. Data were analyzed using Microsoft Excel. SETTING: Bella Coola Medical Clinic on the remote central coast of British Columbia. PARTICIPANTS: Twenty-one patients at the Bella Coola Medical Clinic who were receiving warfarin. MAIN OUTCOME MEASURES: All international normalized ratio (INR) tests over the preceding 12 months were examined for results, time elapsed since previous test, and interval until next scheduled test. RESULTS: An in-range INR rate of 60% is considered acceptable for anticoagulation services. The clinic had performed 406 INR tests on these 21 patients over the last 12 months. We found that 53% of all INR results fell strictly within the recommended therapeutic range. The relative success of anticoagulation management in Bella Coola probably results from several factors. For instance, physicians usually responded to out-of-range INR results with close monitoring: in 71% of cases, follow-up tests were scheduled within 1 week. On average, patients attended 77% of these visits on schedule; 58% of all out-of-range INR results were followed up with retesting within 1 week. CONCLUSION: Our results suggest that primary care physicians can manage anticoagulation adequately, even in remote settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".