A Quality Improvement Initiative to Mitigate Immunotherapy-Related Toxicities among Patients Receiving Immunotherapy using a Novel Grade 2 T
Bibliographic record
Abstract
Immune checkpoint inhibitors have significantly improved outcomes in triple-negative breast cancer, but immune-related toxicities (IRT) remain a major concern. This study aimed to develop and evaluate a grade 2 immunotherapy toxicity screening tool designed to identify and prevent progression of IRTs before they result in hospitalization or irreversible organ damage. The tool was created using patient and healthcare provider handouts adapted from the Cancer Care Ontario Immune Checkpoint Inhibitor Toxicity Management Clinical Practice Guideline. A Plan-Do-Study-Act (PDSA) cycle was employed to test, refine, and implement the tool, with iterative changes based on feedback from healthcare providers and patients. Healthcare providers were engaged through team presentations, and the Patient and Family Advisory Committee was consulted for feedback. Handouts were distributed to patients and healthcare providers, and displayed in chemotherapy suites and clinics. The intervention's effectiveness was assessed through surveys distributed to both groups six months after implementation to gather perceptions and identify barriers to use. There were 71 survey participants in total, 43 healthcare providers and 28 patients. Results revealed that both patients and healthcare providers found the tool easy to follow and would recommend its continued use. However, some patients reported that they did not receive the tool during its rollout. Despite this, the tool was considered helpful by both groups in managing IRTs. Ongoing assessment is necessary to evaluate whether the tool effectively reduces the progression of IRTs leading to hospitalization and organ damage, and further refinements may be needed based on continuous feedback from stakeholders.
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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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".