High incidence of venous thromboembolism despite electronic alerts for thromboprophylaxis in hospitalised cancer patients
Bibliographic record
Abstract
Many cancer patients are at high risk of venous thromboembolism (VTE) during hospitalisation; nevertheless, thromboprophylaxis is frequently underused. Electronic alerts (e-alerts) have been associated with improvement in thromboprophylaxis use and a reduction of the incidence of VTE, both during hospitalisation and after discharge, particularly in the medical setting. However, there are no data regarding the benefit of this tool in cancer patients. Our aim was to evaluate the impact of a computer-alert system for VTE prevention in patients with cancer, particularly in those admitted to the Oncology/Haematology ward, comparing the results with the rest of inpatients at a university teaching hospital. The study included 32,167 adult patients hospitalised during the first semesters of years 2006 to 2010, 9,265 (28.8%) with an active malignancy. Appropriate prophylaxis in medical patients, significantly increased over time (from 40% in 2006 to 57% in 2010) and was maintained over 80% in surgical patients. However, while e-alerts were associated with a reduction of the incidence of VTE during hospitalisation in patients without cancer (odds ratio [OR] 0.31; 95% confidence interval [CI], 0.15-0.64), the impact was modest in cancer patients (OR 0.89; 95% CI, 0.42-1.86) and no benefit was observed in patients admitted to the Oncology/Haematology Departments (OR 1.11; 95% CI, 0.45-2.73). Interestingly, 60% of VTE episodes in cancer patients during recent years developed despite appropriate prophylaxis. Contrary to the impact on hospitalised patients without cancer, implementation of e-alerts for VTE risk did not prevent VTE effectively among those with malignancies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".