Developing a standard approach to immune checkpoint inhibitor toxicity management in Ontario.
Bibliographic record
Abstract
255 Background: The use of immune checkpoint inhibitor medications (ICIs) in the province of Ontario, Canada has increased in volume by almost 4 fold between 2015/16 to 2017/18 and has expanded from use primarily in melanoma to lung, genitourinary, and other cancers. Lack of widespread clinical experience with ICIs and provincial variation in the management of the potentially life-threatening immune-related adverse effects (irAEs) was identified as a safety and quality gap. Cancer Care Ontario (CCO) set out to develop user-friendly health care provider and patient resources to facilitate a standard approach to ICI toxicity management in Ontario. Methods: A multidisciplinary working group of oncology clinicians with ICI experience reviewed available literature and current approaches to ICI toxicity management. An iterative consensus-building process was used to develop a practical guideline. This was circulated to an external expert review panel for content validity. Complementary patient/caregiver information was created based on best practices in health literacy and input from patient and family advisors. All resources were made publicly available via the CCO website and disseminated broadly to relevant stakeholders. Results: A user-friendly clinical practice guideline was created. It contains a description of irAEs associated with ICIs, guidance on the general management of irAEs, detailed algorithms describing the assessment and management of ten specific irAEs, and general considerations for patients on ICIs. A toolkit was developed with direct links to the algorithms, a customizable wallet card, a “Dear Healthcare Professional” letter template, and a patient information sheet. The guideline and toolkit webpages were accessed over 1500 times in the first month, suggesting that broad dissemination has been successful. Informal reports of guideline implementation were received from several Ontario hospitals. Conclusions: Careful analysis of the available literature and application of oncology professionals’ expertise resulted in evidence-informed, consensus-based practical resources to help facilitate safe, standardized ICI toxicity management across the Ontario health care system.
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".