Evaluation of the Interpretation of Serial Ultrasound Examinations in the Diagnosis of Deep Venous Thrombosis in Children: A Retrospective Cohort Study
Bibliographic record
Abstract
PURPOSE: To assess ultrasound intrascan variability and the potential error rate of serial ultrasounds in the diagnosis of deep venous thrombosis in children. METHODS: A retrospective cohort review of imaging results of children having at least 3 serial ultrasound examinations of the same region within a 2-month period. The results were interpreted as either (1) inadequately visualized or (2) the absence or presence of deep venous thrombosis, and were categorized by location. Serial imaging findings then were further categorized based on results and clinical information. RESULTS: Sixty-four patients and 157 vessel segments were included in the study. Deep venous thrombosis was documented in 58 patients. Concordant results were observed in 26 patients (40.1%), clot resolution in 17 patients (26.6%), clot formation in 12 patients (18.8%), and discordant results in 9 patients (14%). Twenty-one of 64 patients (32.8%) had at least 1 vessel inadequately imaged. CONCLUSIONS: The inconsistency of serial ultrasound results in up to 25% of patients calls attention to the potential inaccuracy of ultrasound for diagnosis and follow-up of deep venous thrombosis in children. The high proportion of patients with at least 1 inadequately visualized vessel also highlights the limitation of ultrasound in the diagnosis of pediatric deep venous thrombosis.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".