Cardiovascular drug interventions in the cardio-oncology clinic by a cardiology pharmacist: ICOP-Pharm study
Bibliographic record
Abstract
Background: Cardio-oncology is a rapidly growing field that requires a novel service design to deal with the increasing number of patients. It is reported that the volume of patients at the cardio-oncology clinic in the United Kingdom is 535 patients/5 years and in Canada is 779 patients/7 years. The pharmacist has a role in reducing the consultation time of physicians. Objective: To identify the role of a qualified cardiology pharmacist at the cardio-oncology clinic using a new paradigm based on complementary interventions with the cardiologist for the management of patients with cancer and cardiovascular risk factors and/or cardiovascular diseases (CVRF/CVD). Methods: A prospective observational study was conducted at the cardio-oncology clinic in the Medical City in Baghdad, Iraq between December 2020 and December 2021. Patients with CVRF/CVD were registered. The Iraqi Cardio-Oncology Program-Pharmacist (ICOP-Pharm) paradigm was designed to involve a qualified cardiology pharmacist for initial cardiovascular (CV) drug interventions. Results: Among 333 patients who attended our clinic over the 1-year interval, 200 (60%) CVRF/CVD cases were enrolled in the study, and of them 79 (40%) patients had CV drug interventions. A total of 196 interventions were done, including 147 (75%) cases performed by the cardiology pharmacist, and 92 (63%) of the latter were CV drug initiations. Among the total CVRF/CVD treated initially by the cardiology pharmacist, hypertension 32 (26%) and cancer therapy-related cardiac dysfunction 29 (24%) were the main types. Conclusion: The qualified cardiology pharmacist was responsible for three-quarters of the initial CV drug interventions at the cardio-oncology clinic in a complementary approach to the cardiologist. The role of the cardiology pharmacist in the ICOP-Pharm paradigm may be one of the reasons for the ability of the heart team to manage 3-fold of the patient volume when compared with those in the United Kingdom or Canada.
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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 | 0.016 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".