Do children with central venous line (CVL) dysfunction have increased risk of symptomatic thromboembolism compared to those without CVL-dysfunction, while on cancer therapy?
Bibliographic record
Abstract
BACKGROUND: Thromboembolism (TE) and infection are two common complications of central venous line (CVL). Thrombotic CVL-dysfunction is a common, yet less studied, complication of CVL. Two retrospective studies have reported significant association of CVL-dysfunction and TE. Recent studies indicate association of CVL-related small clot with infection. Infection is the most common cause of non-cancer related mortality in children with cancer. We and others have shown reduced overall survival (OS) in children with cancer and CVL-dysfunction compared to those without CVL-dysfunction. Despite these observations, to date there are no prospective studies to evaluate the clinical significance of CVL-dysfunction and it's impact on the development of TE, infection, or outcome of children with cancer. STUDY DESIGN: This is a prospective, analytical cohort study conducted at five tertiary care pediatric oncology centers in Ontario. Children (≤ 18 years of age) with non-central nervous system cancers and CVL will be eligible for the study. Primary outcome measure is symptomatic TE and secondary outcomes are infection, recurrence of cancer and death due to any cause. Data will be analyzed using regression analyses. DISCUSSION: The overall objective is to delineate the relationship between CVL-dysfunction, infection and TE. The primary aim is to evaluate the role of CVL-dysfunction as a predictor of symptomatic TE in children with cancer. We hypothesize that children with CVL-dysfunction have activation of the coagulation system resulting in an increased risk of symptomatic TE. The secondary aims are to study the impact of CVL-dysfunction on the rate of infection and the survival [OS and event free survival (EFS)] of children with cancer. We postulate that patients with CVL-dysfunction have an occult CVL-related clot which acts as a microbial focus with resultant increased risk of infection. Further, CVL-dysfunction by itself or in combination with associated complications may cause therapy delays resulting in adverse outcome.This study will help to identify children at high risk for TE and infection. Based on the study results, we will design randomized controlled trials of prophylactic anticoagulant therapy to reduce the incidence of TE and infection. This in turn will help to improve the outcome in children with cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".