An International Survey of Health Care Providers Involved in the Management of Cancer Patients Exposed to Cardiotoxic Therapy
Bibliographic record
Abstract
Cardiotoxicity is the second leading cause of morbidity and mortality in cancer survivors. The objective of this international cardiac oncology survey was to gain a better understanding of current knowledge and practice patterns among HCPs involved in the management of cancer patients exposed to potentially cardiotoxic drugs. Between 2012 and 2013, we conducted an email-based survey of HCPs involved in the management of cardiac disease in cancer patients. 393 survey responses were received, of which 77 were from Canadian respondents. The majority of respondents were cardiologists (47%), followed closely by medical oncologists. The majority of respondents agreed that cardiac issues are important to cancer patients (97%). However, only 36% of total respondents agreed with an accepted definition of cardiotoxicity. While 78% of respondents felt that cardiac medications are protective during active cancer treatment, only 51% would consider prescribing these medications up-front in cancer patients. Although results confirm a high level of concern for cardiac safety, there continues to be a lack of consensus on the definition of cardiotoxicity and a discrepancy in clinical practice between cardiologists and oncologists. These differences in opinion require resolution through more effective research collaboration and formulation of evidence-based guidelines.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".