Exploring the Basis and Acceptability of Practice Variation Among Thrombosis Medicine Specialists: A Mixed-Methods Study
Bibliographic record
Abstract
Background: How physicians navigate the uncertainty of diagnosis and management of medical conditions with limited evidence is largely unknown. One lens to look at uncertainty in medicine is through evaluating practice variation among physicians, or their differences in their clinical management. Thrombosis medicine is a subspecialty that focuses on the diagnosis and management of patients with venous thromboembolism or its complications. By better defining the nature and acceptability of practice variation in thrombosis medicine, we can gain insights into how to improve patient care and education of learners in situations when uncertainty exists. Purpose: The aims of our study include: (1) Understand the quantity and type of variation that exists among thrombosis specialists; (2) Determine the level of acceptability of practice variation among thrombosis specialists and (3) Identify any guiding principles that thrombosis specialists used when making decisions in areas of clinical uncertainty. Methods: Five challenging clinical vignettes were presented to thrombosis specialists in Ottawa, Canada in 60 minute semi-structured interviews. The vignettes included the management of symptomatic superficial vein thrombosis, a pregnant patient with a pulmonary embolism, a patient with cirrhosis and a portal vein thrombosis, a patient with suspected Essential Thrombocythemia and a deep vein thrombosis, and a patient with past pregnancy losses and positive antiphospholipid antibodies. All management options described in the interviews were included in a follow-up anonymous online questionnaire to the same specialists, in order to delineate the acceptability of other specialists' answers. Interview data were audio recorded and transcribed. Themes were developed through open coding with constant comparative analysis, and through focused coding using the conceptual framework Principles and Preferences. Questionnaire data was analyzed with descriptive statistics, with means, standard deviations (SD) and ranges for continuous variables, and frequencies with percentages for dichotomous variables. Results: All ten (100%) thrombosis specialists completed interviews and 8 completed the follow-up survey. Out of 110 possible management options identified across 5 clinical vignettes, 88 (80%) management options were recommended by at least 1 thrombosis specialist. Complete consensus where all specialists recommended a management option was reached in only 3 (3.4%) items. Consensus, defined as >50% of participants recommending or strongly recommending a management option, was achieved in 23 (26%) of the management options. Despite wide practice variation, there was a high level of acceptability of the different management options. Analysis of interview data identified how specialists managed clinical uncertainty, which included five themes: (1) Knowing the latest evidence or relying on colleagues' expertise; (2) Past experiences; (3) Clinical gestalt and common sense; (4) "Benefits outweigh the risks" and (5) Improving the patient experience. Conclusions: While there was a wide range of practice variation among thrombosis specialists in cases of clinical uncertainty, there was a high level of acceptability of different management options. Research is needed on how this affects patient care and the instruction and assessment of learners. Disclosures No relevant conflicts of interest to declare.
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 | 0.056 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".