Evaluation and clinical impact of a pharmacist-led, interdisciplinary service focusing on education, monitoring and toxicity management of immune checkpoint inhibitors
Bibliographic record
Abstract
Introduction Immune-related adverse events are complications of immune checkpoint inhibitors which require robust patient education and proactive follow-up to ensure timely identification and management. Oncology pharmacist practice models with other anticancer modalities have been well documented, but there is limited evidence assessing the spectrum of pharmacist interventions in patients receiving immune checkpoint inhibitor(s) and the impact of these interventions on patient outcomes. Methods Patients initiated on immune checkpoint inhibitor(s) from 1 January 2016 to 31 August 2019 were included for data collection and analysis. Part 1 featured an intensive pharmacist follow-up cohort (study cohort) and summarized pharmacist interventions. Part 2 compared patient outcomes between the study cohort and a standard of care cohort (control cohort) from a different oncology centre. Patient outcomes included emergency department visits not resulting in admission, hospitalizations due to immune-related adverse event(s), immune checkpoint inhibitor cycles received, treatment discontinuation due to immune-related adverse event(s), completion of finite programmed death-1/death-1 ligand treatment course and completion of ipilimumab. Clinical outcomes were compared using a retrospective, matched cohort design based on age, cancer diagnosis and immune checkpoint inhibitor(s). Results A total of 143 patients were included in Part 1 encompassing 1664 pharmacist recommendations across 11 categories. The matched cohort yielded 92 matches (n = 184) with a higher odds of immune checkpoint inhibitor discontinuation due to immune-related adverse event(s) in the control cohort (odds ratio (OR) (95% confidence interval (CI)) = 5.5 (1.2−24.8); p = 0.022). Conclusion Intensive immune-related adverse event education, proactive follow-up and immune-related adverse event management by pharmacists result in clinically meaningful interventions which correlate to improved patient outcomes, namely lower odds of treatment discontinuation due to immune-related adverse event(s).
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".