Bibliographic record
Abstract
Objective To determine whether community-based, nurse-led monitoring of the international normalized ratio (INR) in patients requiring long-term warfarin therapy was comparable to traditional physician monitoring. Design A retrospective cohort analysis of patients taking long-term warfarin therapy. Setting The study used data gathered from 3 family medicine clinics in a primary care network in Edmonton, Alta. Participants Medical records of patients currently taking warfarin were examined. Intervention Implementation of nurse-led monitoring in a primary care network in place of standard family physician INR monitoring. Main outcome measures The degree of INR control before and after the implementation of nurse-run INR monitoring was assessed. The average proportion of time spent outside of therapeutic INR ranges, as well as the average number of days between successive INR readings, was calculated and compared. The degree of control placed patients into either a good-control group (out of range ≤ 25% of the time) or a moderate-control group (out of range > 25% of the time) and these groups were compared. Results Before nurse monitoring, INR values were out of range 20.4% of the time; after nurse monitoring they were out of range 19.2% of the time ( P = .115); the time between sequential INR readings also did not differ before and after implementation of nurse monitoring (23.9 vs 21.6 days, P = .789). Conclusion Nurse-led monitoring of INR is as effective as traditional physician monitoring. Advantages of nurse-led monitoring might include freeing family physicians to see more patients or to spend less time at work. It might also represent potential cost savings.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".