systematic review of clinical decision rules used for diagnosing pulmonary embolism in the pediatric opulation
Bibliographic record
Abstract
Objective: This review aims to evaluate the diagnostic accuracy of existing, adult clinical decision tools for pulmonary embolism, in the pediatric population. Methods: A systematic search and screening of the Pubmed, Embase, CINAHL, and Cochrane databases was done in January 2018. Studies evaluating the diagnostic accuracy of clinical decision tools and/or risk factors and clinical features for pulmonary embolism in the pediatric population were included. The measures of diagnostic accuracy of clinical decision tools were calculated. The pooled sensitivity and specificity of risk factors were calculated using a bivariate random effects model. All included studies were assessed for quality using QUADAS-2. Results: Six studies were included: three case-control and three retrospective cohort studies. We found that no standard clinical decision tool for pulmonary embolism has been evaluated in the pediatric population. As well, adult clinical decision tools have low diagnostic utility in pediatrics. Conclusion: Adult clinical decision tools should not be used for pediatric patients. There was no single risk factor or clinical feature displaying reliable sensitivity; however, a central venous line, a recent surgery, or the finding of hemoptysis, all have a positive likelihood ratio greater than two, demonstrating their potential diagnostic utility. Large, prospective cohort studies are needed.
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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 | 0.019 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".