The landscape of cardiovascular care in pediatric cancer patients and survivors: a survey by the ACC Pediatric Cardio-Oncology Work Group
Bibliographic record
Abstract
Abstract Objective To enhance the understanding of cardiovascular care delivery in childhood cancer patients and survivors. Study design A 20-question survey was created by the Pediatric Cardio-oncology Work Group of the American College of Cardiology (ACC) Cardio-oncology Section to assess the care, management, and surveillance tools utilized to manage pediatric/young adult cardio-oncology patients. The survey distribution was a collaborative effort between Cardio-oncology Section and membership of the Adult Congenital and Pediatric Cardiology Section (ACPC) of the ACC. Results Sixty-five individuals, all self-identified as physicians, responded to the survey. Most respondents ( n = 58,89%) indicated childhood cancer patients are regularly screened prior to and during cancer therapy at their centers, predominantly by electrocardiogram (75%), standard echocardiogram (58%) and advanced echocardiogram (50%) (i.e. strain, stress echo). Evaluation by a cardiologist prior to/during therapy was reported by only 8(12%) respondents, as compared to post-therapy which was reported by 28 (43%, p < 0.01). The most common indications for referral to cardiology at pediatric centers were abnormal test results ( n = 31,48%) and history of chemotherapy exposure ( n = 27,42%). Of note, during post-treatment counseling, common cardiovascular risk-factors like blood pressure (31,48%), lipid control (22,34%), obesity & smoking (30,46%) and diet/exercise/weight loss (30,46%) were addressed by fewer respondents than was LV function (72%). Conclusions The survey data demonstrates that pediatric cancer patients are being screened by EKG and/or imaging prior to/during therapy at most centers. Our data, however, highlight the potential for greater involvement of a cardiovascular specialist for pre-treatment evaluation process, and for more systematic cardiac risk factor counseling in posttreatment cancer survivors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".