Clinical decision support to enhance venous thromboembolism pharmacoprophylaxis prescribing for pediatric inpatients with COVID‐19
Bibliographic record
Abstract
OBJECTIVE: To design and evaluate a clinical decision support (CDS) module to improve guideline concordant venous thromboembolism (VTE) pharmacoprophylaxis prescribing for pediatric inpatients with COVID-19. MATERIALS AND METHODS: The proportion of patients who met our institutional clinical practice guideline's (CPG) criteria for VTE prophylaxis was compared to those who triggered a CDS alert, indicating the patient needed VTE prophylaxis, and to those who were prescribed prophylaxis pre and post the launch of a new VTE CDS module to support VTE pharmacoprophylaxis prescribing. The sensitivity, specificity, positive predictive value (PPV), negative predictive value, F1-score and accuracy of the tool were calculated for the pre- and post-intervention periods using the CPG recommendation as the gold standard. Accuracy was defined as the sum of the true positives and true negatives over the sum of the true positives, false positives, true negatives, and false negatives. Logistic regression was used to identify variables associated with correct thromboprophylaxis prescribing. RESULTS: A significant increase in the proportion of patients triggering a CDS alert occurred in the post-intervention period (44.3% vs. 6.9%, p < .001); however, no reciprocal increase in VTE prophylaxis prescribing was achieved (36.6% vs. 40.9%, p = .53). The updated CDS module had an improved sensitivity (55.0% vs. 13.3%), NPV (44.9% vs. 36.3%), F1-score (66.7% vs. 23.5%), and accuracy (62.5% vs. 42.0%), but an inferior specificity (78.6% vs. 100%) and PPV (84.6% vs. 100%). DISCUSSION: The updated CDS model had an improved accuracy and overall performance in correctly identifying patients requiring VTE prophylaxis. Despite an increase in correct patient identification by the CDS module, the proportion of patients receiving appropriate pharmacologic prophylaxis did not change. CONCLUSION: CDS tools to support correct VTE prophylaxis prescribing need ongoing refinement and validation to maximize clinical utility.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".