Clinical and Health System Determinants of Venous Thromboembolism Event Rates After Hip Arthroplasty
Bibliographic record
Abstract
BACKGROUND: Routinely collected hospital data provide increasing opportunities to assess the performance of health care systems. Several factors may, however, influence performance measures and their interpretation between countries. OBJECTIVE: We compared the occurrence of in-hospital venous thromboembolism (VTE) in patients undergoing hip replacement across 5 countries and explored factors that could explain differences across these countries. METHODS: We performed cross-sectional studies independently in 5 countries: Canada; France; New Zealand; the state of California; and Switzerland. We first calculated the proportion of hospital inpatients with at least one deep vein thrombosis (DVT) or pulmonary embolism by using numerator codes from the corresponding Patient Safety Indicator. We then compared estimates from each country against a reference value (benchmark) that displayed the baseline risk of VTE in such patients. Finally, we explored length of stay, number of secondary diagnoses coded, and systematic use of ultrasound to detect DVT as potential factors that could explain between-country differences. RESULTS: The rates of VTE were 0.16% in Canada, 1.41% in France, 0.84% in New Zealand, 0.66% in California, and 0.37% in Switzerland, while the benchmark was 0.58% (95% confidence interval, 0.35-0.81). Factors that could partially explain differences in VTE rates between countries were hospital length of stay, number of secondary diagnoses coded, and proportion of patients who received lower limb ultrasound to screen for DVT systematically before hospital discharge. An exploration of the French data showed that the systematic use of ultrasound may be associated with over detection of DVT but not pulmonary embolism. CONCLUSIONS: In-hospital VTE rates after arthroplasty vary widely across countries, and a combination of clinical, data-related, and health system factors explain some of the variations in VTE rates across 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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".