Developing a standardized approach to cancer medication-related infusion reaction prevention and management in Ontario.
Bibliographic record
Abstract
255 Background: Cancer medication-related infusion reactions (CMIRs) can lead to treatment delays, switching to less optimal therapy, or discontinuation. Variation in the prevention, management and reporting of CMIRs was identified as a quality and safety gap by Ontario clinicians. This initiative aimed to facilitate a standardized approach to CMIRs in Ontario through the development of user-friendly health-care provider and patient resources. Methods: A multidisciplinary working group of 15 oncology clinicians (oncologists, pharmacists, nurses, administrators) with experience in the management of CMIRs reviewed available literature on CMIR assessment, prevention and management. Recommendations were based on best available evidence and group consensus when only low-level evidence was available. Final guideline content was reviewed and validated by external experts. Complementary patient information was created based on best practices in health literacy and input from patient education experts, patients and caregivers. A mechanism for system-wide reporting to track CMIRs was explored. Results: A CMIR clinical practice guideline was created. Definitions, risk factors, prophylaxis strategies, an acute management algorithm and desensitization protocols are described. A CMIR severity grading system was proposed. A toolkit was developed and contains a table outlining risk factors, mechanism, incidence, symptoms, onset, prophylaxis, acute management and re-challenge by specific drug and a tool for calculating a three-bag 12-step desensitization. Patient-friendly information was also created. A new data element for collection of CMIR incidence and severity was added to the provincial Activity Level Reporting (ALR) data set to track CMIR trends. Conclusions: A review of current evidence and expertise from oncology clinicians resulted in an evidence-informed, consensus-based guideline. This guideline and the accompanying resources can help to facilitate safe and standardized prevention and management of CMIRs across Ontario. The collection of system-wide CMIR data based on the proposed grading system will inform future quality and safety improvement initiatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".