Lessons Learnt in the First Year of an Australian Pediatric Cardio Oncology Clinic
Bibliographic record
Abstract
Abstract Background: Modern oncological therapies together with chemotherapy and radiotherapy have broadened the agents that can cause cardiac sequelae, which can manifest for pediatric oncology patients while on active treatment. Recommendations for high-risk patients who should be monitored in a pediatric cardio-oncology clinic have previously been developed by expert Delphi consensus by our group. In 2022 we opened our first multidisciplinary pediatric cardio-oncology clinic adhering to these recommendations in surveillance and management. Objectives: Our pediatric cardio-oncology clinic aimed to: (i) Document cardiovascular toxicities observed within a pediatric cardio-oncology clinic and (ii) Evaluate the applicability of the Australian and New Zealand Pediatric Cardio-Oncology recommendations. Methods: Monthly multidisciplinary cardio-oncology clinics were conducted in an Australian tertiary pediatric hospital. Structured standardised approaches to assessment were built into the electronic medical record (EMR). All patients underwent baseline echocardiogram and electrocardiogram assessment together with vital signs in conjunction with standard history and examination. Results: Nineteen (54%) individuals had a documented cardiovascular toxicity or pre-existing risk factor prior to referral. The two most common cardiovascular toxicities documented during clinic review included Left Ventricular Dysfunction (LVD) and hypertension. Of note 3 (8.1%) patients had CTCAE grade III LVD. An additional 10 (27%) patients reviewed in clinic had CTCAE grade I hypertension. None of these patients had hypertension noted within their referral. Cascade testing for cardiac history was warranted in 2 (5.4%) of patients. Conclusions: Pediatric cardio-oncology clinics are likely beneficial to documenting previously unrecognised cardiotoxicity and relevant cardiac family histories, whilst providing an opportunity to address lifestyle risk factors.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".