Working toward a System-wide Venous Thromboembolism Prophylaxis Strategy: Experience from a Multisite Health Region
Bibliographic record
Abstract
Background Venous thromboembolism (VTE), comprised of both deep vein thrombosis (DVT) and pulmonary embolism (PE), is a common complication for hospitalized patients. Clear guidance is available to practitioners in regard to risk factors for the development of VTE as well as strategies to decrease its prevalence. Despite knowing who is at risk and how to prevent VTE, practitioners provide adequate measures to only half of the patients who are eligible for VTE prophylaxis. Pharmacy practitioners within the Regina Qu'Appelle Health Region (RQHR) have been actively involved in improving VTE prophylaxis for inpatients over the past 10 years. Objective To improve the rate of VTE prophylaxis within the RQHR, thereby improving patient safety. Methods The strategy involved 3 phases: a preparation phase, an active intervention phase, and a maintenance and improvement phase. The preparation phase included education and participation in a national registry along with a residency project. The intervention phase consisted of a number of strategies in conjunction with 1-day VTE prophylaxis audits, and the maintenance phase consisted of ongoing educational initiatives and audits. Results From January 2005 to January 2009, the percentage of patients being appropriately managed for VTE prophylaxis within the RQHR improved from 62% to 94% ( P < .005). Looking specifically at our medical and surgical populations, rates increased from 47% to 90% ( P < .005) and 79% to 97% ( P < .005), respectively. Conclusion The strategy was successful in improving VTE prophylaxis in the inpatient population.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".