An assessment of evidence to inform best practice for the communication of acute venous thromboembolism diagnosis: a scoping review
Bibliographic record
Abstract
Abstract Background Physician communication with patients is a key aspect of excellent care. Scant evidence exists to inform best practice for physician communication in patients diagnosed with pulmonary embolism and deep vein thrombosis, collectively referred to as venous thromboembolism (VTE). The aim of this study was to summarize the existing literature on best practices for communication between healthcare providers and patients newly diagnosed with VTE. Methods We performed a scoping review of the extant literature on best practice for physician patient communication and the diagnosis and management of VTE. Manuscripts on communication between healthcare professionals and patients with acute vascular diseases, including VTE, were eligible. Two authors independently reviewed titles, and consensus determined article inclusion. The manuscripts were further categorized into two main categories: best practice in communication and unmet needs in communication. Data aggregation was achieved by a modified thematic synthesis. Results Among 345 initial publications, 22 manuscripts met inclusion criteria with 11 that addressed VTE, five pulmonary embolism, four deep vein thrombosis, one atrial fibrillation, and one acute coronary syndrome. Eleven manuscripts addressed communication of VTE diagnosis, while 12 focused on communication of VTE treatment. Eleven manuscripts identified unmet communication needs, and 14 addressed best practice. Our review shows that good communication surrounding the VTE diagnosis and treatment can enhance satisfaction while suboptimal communication can incur emotional, cognitive, behavioral, social, and health-systems adverse effects. Conclusion Scant literature guides best practices for communicating VTE diagnosis and treatment. Further research is necessary to establish practices for improving communication with VTE patients.
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 | 0.132 | 0.442 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.056 | 0.033 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.005 |
| 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".