Incidência de Complicações Cardiovasculares em Pacientes Pediátricos Tratados com Antraciclinas em um Centro Oncológico Brasileiro
Bibliographic record
Abstract
BACKGROUND: The introduction of anthracyclines in the treatment of children and adolescents with cancer has promoted a significant increase in survival, but also in morbidity and mortality rates due to cardiovascular (CV) complications. OBJECTIVES: To determine the cardiovascular profile of pediatric patients treated with anthracyclines at a cancer center in Brazil and the incidence of CV complications. METHODS: The following data were collected from the medical records of patients of both sexes, aged younger than 19 years - frequency and form of clinical presentation of general CV complications (G1) and CV complications related to ventricular dysfunction (G2) - and correlated with risk factors, age range and vital status, cardiovascular and cardioprotective medications. A p<0.05 was considered statistically significant. RESULTS: A total of 326 patients were included, 214 (65.6%) were younger than 10 years and 192 (58.9%) of male sex. G1 complications occurred in 141 (43.3%) patients, and the most frequent was systemic arterial hypertension; G2 complications occurred in 84 patients (25.8%). Cumulative dose (CD) of anthracyclines > 250mg/m2 was used in 26.7% of patients and the association of G2 complications with this CD was not statistically significant (p=0.305; OR=1.330 and [95% CI = 0.770- 2.296]). The most used cardiac medications were diuretics (34.7% of patients). CONCLUSIONS: In accordance with literature, the study showed a high incidence of CV complications in the treatment of children and adolescents with cancer, with general CV complications as the most prevalent.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".