Screening and management of dyslipidemia in oncologic patients undergoing cardiotoxic therapies: results from an Italian survey
Bibliographic record
Abstract
BACKGROUND: Baseline cardiovascular risk factors correction is recommended in all cancer patients undergoing potentially cardiotoxic therapies. Despite available guidelines, real-world data on dyslipidemia prevalence and management in the oncologic population are still sparse. METHODS: This survey was an Italian, investigator-initiated survey initially designed and drafted by the Cardio-Oncology section of the Associazione Nazionale Medici Cardiologi Ospedalieri (ANMCO), comprising 10 individual multi-choice questions and spread after validation through the ANMCO mailing list. The survey was sent to cardiologists working in cardio-oncology units and/or managing patients with cancer. RESULTS: Our survey included 139 Italian cardiologists. The majority of them routinely ask for the baseline lipidic profile of their patients, regardless of previous clinical history and planned treatment. According to our participants, the estimated prevalence of dyslipidemia in this population is between 20% and 60%. Although this high prevalence, our results highlight that there is poor harmony in terms of scores for CV risk prediction used in clinical practice to guide drug prescription and baseline therapy optimization. On the same line, coronary artery calcium score is poorly used in this setting. At the same time, more than 30% of interrogated physicians do not prescribe adequate statin doses, even though necessary, and have uncertainties on the use of other anti-dyslipidemic drugs in this population. CONCLUSIONS: Our results highlight the necessity of strong evidences on dyslipidemia screening and management in the cancer population, as well as the need of knowledge diffusion from scientific societies to clinicians treating these patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".