Diagnostic value of D‐dimers for limb deep vein thrombosis in children: A prospective study
Bibliographic record
Abstract
The present study sought to evaluate the discriminative and predictive ability of D-dimer for pediatric limb DVT. Children aged 28 days-18 years requiring imaging to rule out limb DVT, as per the treating clinical team, were enrolled in the study. The outcome was ultrasound proven DVT. The D-dimer levels were obtained around the time of imaging. Receiver operating characteristic (ROC) curves and logistic regression models were used for data analyses. In total, 296 patients were enrolled between 2017-2020; 204 patients were diagnosed with DVT (DVT[+]). Median D-dimer levels were 2.3 μg/ml FEU (25th-75th percentile 0.9-3.9) among DVT(+) and 1.9 μg/ml FEU (25th-75th percentile 0.8-4.0) among DVT(-) patients (p = 0.60). The area under the ROC curve (AUC) was 0.52 (95% confidence interval [CI] 0.45-0.59). The odds ratio for D-dimer levels was 1.00 (95% CI 0.99-1.01), holding confounders constant. In a sub-group exploratory analysis including 23 patients with no underlying conditions or co-morbidities, the AUC curve was 0.90 (95% CI 0.76-1.00). In conclusion, in this prospective cohort study of consecutive children with suspected limb DVT, D-dimer levels had poor discriminative and predictive ability for DVT. However, D-dimer levels showed better discriminative and predictive ability for DVT in an exploratory sample of patients with no underlying conditions or co-morbidities at the time of diagnosis.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".