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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".