ClotAssist: A program to treat cancer-associated thrombosis in an outpatient pharmacy setting
Bibliographic record
Abstract
Stable cancer patients diagnosed with a pulmonary embolus or deep vein thrombosis are commonly referred to the emergency department for management. This practice strains an already overburdened emergency department and is associated with long wait times and poor disease/injection education for patients. This pilot study sought to determine if stable cancer patients with newly diagnosed cancer-associated thrombosis could be effectively managed by community-based pharmacists who followed an evidence-based protocol to prescribe and initiate low-molecular weight heparin therapy. We hypothesized that this novel care pathway could provide faster patient care with more comprehensive disease education, self-injection training, and follow-up. Fifty-five patients with various cancers, including gastroesophageal, urogenital, breast, brain, and lung were enrolled into this pilot study. We observed that this alternative first-dose treatment pathway provided safe and effective treatment of venous thromboembolism combined with excellent patient satisfaction. Following their interaction with the pharmacist, patients felt confident about their ability to self-inject and about their venous thromboembolism management overall. No occurrences of bleeding or other side-effects were observed. This pilot study demonstrates that community-based pharmacists are capable of delivering complex care services in the outpatient environment, particularly in the management of venous thromboembolism.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".