Uptake of new treatment strategies for deep vein thrombosis: an international audit
Bibliographic record
Abstract
OBJECTIVE: Study of the uptake of new medical technologies provides useful information on the transfer of published evidence into usual practice. We conducted an audit of selected hospitals in three countries (Canada, France, and Switzerland) to identify clinical predictors of low-molecular-weight (LMW) heparin use and outpatient treatment, and to compare the pace of uptake of these new therapeutic approaches across hospitals. DESIGN: Historical review of medical records. SETTING AND PARTICIPANTS: We reviewed the medical records of 3043 patients diagnosed with deep vein thrombosis (DVT) in five Canadian, two French, and two Swiss teaching hospitals from 1994 to 1998. Measures. We explored independent clinical variables associated with LMW heparin use and outpatient treatment, and determined crude and adjusted rates of LMW heparin use and outpatient treatment across hospitals. RESULTS: For the years studied, the overall rates of LMW heparin use and outpatient treatment in the study sample were 34.1 and 15.8%, respectively, with higher rates of use in later years. Many comorbidities were negatively associated with outpatient treatment, and risk-adjusted rates of use of these new approaches varied significantly across hospitals. CONCLUSION: There has been a relatively rapid uptake of LMW heparins and outpatient treatment for DVT in their early years of availability, but the pace of uptake has varied considerably across hospitals and countries.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".