Optimizing pharmacologic thromboprophylaxis use in pediatric orthopedic surgical patients through implementation of a perioperative venous thromboembolism risk screening tool
Bibliographic record
Abstract
BACKGROUND: Although rare, venous thromboembolic events (VTE) are a significant challenge in pediatric orthopedic surgical patients (POSP). A VTE thromboprophylaxis screening tool was developed and implemented in POSPs at the IWK Health Centre since October 2016. OBJECTIVES: This retrospective cohort study was designed to evaluate and assess the impact of the VTE thromboprophylaxis screening tool in terms of use of thromboprophylaxis in POSP. METHODS: Using the tool, POSPs were screened and were categorized into risk groups. Patient groups were compared and spearman correlation analysis was performed to show the strength of association between risk factors and thromboprophylaxis. Retrospective screening of pre-algorithm patients who received thromboprophylaxis was done to further assess the screening tool. RESULTS: After the implementation of the VTE thromboprophylaxis screening tool in POSPs, there was a 47.9% reduction in the use of thromboprophylaxis (P = 0.046) as compared with before. Neither VTE nor significant bleeding complications occurred before or after screening tool implementation. Compliance with the screening tool was excellent (100% of patients in the high-risk category received thromboprophylaxis). High-risk patients were more likely to have body mass index > 30 (35.7%), limited/altered mobility (57.1%), and to be undergoing a complicated/repeat surgery (64.3%). CONCLUSIONS: The present study demonstrates successful implementation of a VTE thromboprophylaxis screening tool that resulted in significant reduction in use of thromboprophylaxis in POSPs with no increase in VTE or change in bleeding complications.
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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 | 0.006 |
| 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 | 0.000 | 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".