Utility of a Nurse-Led Pathway for Patients with Acute Venous Thromboembolism Discharged on Rivaroxaban: A Prospective Cohort Study
Bibliographic record
Abstract
Abstract The highest risk of adverse events for patients with acute venous thromboembolism (VTE) is during the early anticoagulation period. However, no established model exists for early clinical monitoring of patients treated with non–vitamin K antagonist oral anticoagulants (NOACs). The authors' aim was to evaluate the utility of a nurse-led pathway to minimize adverse events in acute VTE patients starting on rivaroxaban. The rivaroxaban VTE treatment pathway is a prospective cohort study of consecutive patients with objectively confirmed VTE between July 2015 and May 2017. Primary outcome was the proportion of patients identified at major risk of adverse events (bleeding or recurrent VTE). Secondary outcomes were rates of interventions, major or clinically relevant nonmajor bleeding (CRNMB), recurrent VTE, and all-cause mortality at 90 days. Among 304 participants, 5% (n = 15) were identified to be at major and 9% (n = 28) at possible risk for adverse events. Appropriate interventions to prevent harm were required in 40 patients. Rates of major bleeding, CRNMB, recurrence, and all-cause mortality were 0.3% (95% confidence interval [CI]: 0.1–1.8), 7.2% (95% CI: 4.8–10.7), 1.0 (95% CI: 0.3–2.9), and 1.6% (95% CI: 0.7–3.8), respectively. In conclusion, following discharge of acute VTE patients, a nurse-led pathway identified one in seven (14%) patients at major or possible risk of adverse events. Preemptive interventions to reduce harm translated into the low rates of bleeding and recurrence. The authors' experience highlights the feasibility and importance of a structured clinical surveillance pathway for acute VTE patients initiating NOAC therapy.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".