Investigating the Use of a Patient Tool to Identify Grade 2 Immunotoxicity
Bibliographic record
Abstract
Grade 2 immunotoxicity refers to moderate immune-related side effects caused by immunotherapy. These side effects can manifest in various ways, including skin rashes, gastrointestinal issues, joint pain, and more. Grade 2 toxicity is the first grade of toxicity that requires withholding treatment and steroid intervention, which is why it is the focus of this study. These toxicities often escalate to higher grades before detection due to the absence of standardized guidelines for patients to recognize them. Baseline data from Windsor Regional Hospital revealed a critical gap: none of the 15 prescribing oncologists had regular written guidelines for patients to identify these toxicities. As a result, 24% of immunotherapy infusions led to emergency room visits, with 5% resulting in hospitalizations between March and June 2022. The considerable impact of delayed intervention underscores the urgency of addressing this issue. Our project aims to develop a user-friendly tool to identify grade 2 immunotoxicity. This tool will empower both patients and healthcare professionals to recognize grade 2 toxicities early, allowing for timely intervention. We will create a one-page infographic, available in multiple languages, to help patients self-identify grade 2 toxicities. This tool will be distributed through paper-based posters. We will evaluate its effectiveness through feedback from healthcare professionals. Successful implementation of our tool is expected to reduce emergency room visits and hospitalizations, enhance patient therapy completion rates, and improve patient experiences. The project's scalability will enable easy adoption in other cancer programs across Ontario and CaEli.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".