Bibliographic record
Abstract
The prevention and management of venous thromboembolism have become increasingly important clinical issues that cross many professional and subspecialty boundaries. Better identification of risk factors, improvements in the diagnosis and treatment of thromboembolic disorders, and greatly expanded knowledge related to thromboprophylaxis have presented new challenges for the delivery of evidencebased care. Accumulating data and subsequent recommendations in this area are collected every 3 years in the American College of Chest Physicians Consensus Conference on Antithrombotic Therapy and published as a supplement to the journal Chest, 1 an issue that is widely read by interested hospital pharmacists and physicians. There are many centres of excellence in clinical thromboembolism in North America, all of which are clustered at tertiary care teaching hospitals. At the vast majority of community hospitals, thrombosis prevention and management are not the responsibility of any one practitioner but rest mainly in the hands of physicians in a variety of specialties including hematology, cardiology, internal medicine, orthopedics, and vascular surgery. Most long-term anticoagulation for ambulatory patients is managed by family physicians. Yet we believe that pharmacists are well suited to take on responsibilities in this area, and our purpose in writing this editorial is to try to expedite this transfer of responsibility from physician to pharmacist. Recent studies and surveys have shown that the application of evidence-based practice in several aspects of management of venous thromboembolism could be significantly improved. For example, thromboprophylaxis is frequently underutilized both in general medical inpatients and after high-risk abdominal surgery, whereas the reversal of warfarin overanticoagulation
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".