Cardio-oncology Clinical Assessment and Screening in Patients Undergoing High Toxicity Chemotherapy: A Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To describe the clinical characteristics and cardio-oncological assessment in patients undertaking highly toxic chemotherapy and/or chest radiotherapy in a high-complexity hospital. METHODS: A single-center retrospective cohort study was carried out between January 1st, 2017 and December 31st, 2019. The medical records of patients with solid or hematological neoplasms were reviewed. Descriptive information was obtained on demographic characteristics, chemotherapeutic agents, pre-chemotherapy cardiovascular (CV) evaluation, and CV outcomes. The risk of complications was assessed using the Mayo Clinic risk score. RESULTS: A total of 499 patients were included, the most common neoplasm was non-Hodgkin's lymphoma (21.6%), followed by breast cancer (19.4%). A very high risk of cardiotoxicity was present in 44.1% and 90% were not evaluated by cardiology. Pre-chemotherapy echocardiography was obtained in 65%, but only 19.4% underwent echocardiographic control after finishing chemotherapy. The most frequent CV outcomes were chemotherapy-related systolic dysfunction (4.4%) and rhythm disturbances (2.8%), with atrial fibrillation and atrial flutter being the most frequent arrhythmias. CONCLUSION: Despite the recognized CV toxicity of chemotherapeutic drugs, the majority of patients receiving highly toxic regimens at high risk of CV complications are not previously evaluated by a cardiologist and the CV workup was not routinely used in our study. The implementation of cardio-oncology programs will facilitate the identification of high-risk patients, aiming to detect and treat complications early.
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 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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".