The Cardio-Oncology Patients—What They Know and What They Should Know
Bibliographic record
Abstract
The growing number of patients after oncological treatment makes knowledge about potential cardiovascular complications of cancer therapy particularly important. Early recognition of symptoms enables the rapid initiation of appropriate therapy and improves outcomes. Education in this field increases awareness of the need for regular cardiology follow-up and adherence to health recommendations. It is advisable for patient education on the risk of cardiotoxicity to be included during visits with both the oncologist and the cardiologist. A self-developed questionnaire was used. It consisted of 40 questions (including 16 from the Health Behavior Scale) and 8 additional sociodemographic questions. An anonymous questionnaire was completed by 243 patients of the cardio-oncology outpatient clinic operating within the Department of Cardiology in Poland. In the survey conducted, patients were asked to define the concept of cardio-oncology; only 23.5% of respondents provided a correct answer. The highest level of awareness was observed among individuals under the age of 40 (p = 0.001) and of higher education levels (p < 0.001). Better knowledge was also noted among respondents who recalled being informed by their doctor about complications (p < 0.001) and among those who had undergone cardiological examinations (p = 0.005). The findings further revealed that respondents who recognized the importance of cardiac monitoring following therapy were significantly more likely to engage in health behaviors (p < 0.001). Particularly concerning was the limited communication regarding cardiovascular risks associated with cancer treatment. Only 24.3% of patients reported having been informed (or recalled being informed) by their oncologist about the potential cardiotoxic effects of anticancer drugs. Approximately one-third of respondents (32%) had not been referred for a cardiology consultation during their cancer treatment. Despite this, an overwhelming majority (95.5%) expressed the belief that a cardiologist should assess all oncology patients. These findings underscore critical deficiencies in patients’ education within the field of cardio-oncology. Health education interventions during oncological follow-up visits are needed
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".