Epidemiology of clinically unsuspected venous thromboembolism in children with cancer: A population‐based study from Maritimes, Canada
Bibliographic record
Abstract
Inconsistencies in the definition of clinically unsuspected venous thromboembolism (VTE) in pediatric patients recently led to the recommendation of standardizing this terminology. Clinically unsuspected VTE (cuVTE) is defined as the presence of VTE on diagnostic imaging performed for indications unrelated to VTE in a patient without symptoms or clinical history of VTE. The prevalence of cuVTE in pediatric cancer patients is unclear. Therefore, the main objective of our study was to determine the prevalence of cuVTE in pediatric cancer patients. All patients 0-18 years old, treated at the IWK in Halifax, Nova Scotia, from August 2005 through December 2019 with a known cancer diagnosis and at least one imaging study were eligible (n = 743). All radiology reports available for these patients were reviewed (n = 18,120). The VTE event was labeled a priori as cuVTE event for radiology reports that included descriptive texts indicating a diagnosis of thrombosis including thrombus, central venous catheter-related, thrombosed aneurysm, tumor thrombosis, non-occlusive thrombus, intraluminal filling defect, or small fragment clot for patients without documentation of clinical history and or signs of VTE. A total of 18,120 radiology reports were included in the review. The prevalence of cuVTE was 5.5% (41/743). Echocardiography and computed tomography had the highest rate of cuVTE detection, and the most common terminologies used to diagnose cuVTE were thrombus and non-occlusive thrombus. The diagnosis of cuVTE was not associated with age, sex, and type of cancer. Future efforts should focus on streamlining radiology reports to characterize thrombi. The clinical significance of these cuVTE findings and their application to management, post-thrombotic syndrome, and survival compared to cases with symptomatic VTE and patients without VTE should be further studied.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".