Improving anti-emetics in chemotherapy induced nausea and vomiting.
Bibliographic record
Abstract
220 Background: Chemotherapy induced nausea and vomiting (CINV) remains one of the most feared treatment-related toxicities in cancer patients. CINV has been shown to decrease quality of life and to increase dose modifications and unplanned hospital visits. Cancer Care Ontario (CCO) and the American Society of Clinical Oncology (ASCO) updated their CINV guidelines in 2013. These changes included a reclassification of many regimens from moderate (MEC) to highly emetogenic (HEC) and a decrease in the duration of serotonin inhibitors (5HT3i). Uptake of the new guidelines at Trillium Health Partners has been slow. We aimed to improve CINV by increasing the percentage of patients who received guideline concordant anti-emetics with their first cycle of HEC/MEC chemotherapy. Methods: The first 25 patients started on MEC/HEC chemotherapy during 3 time periods (pre-guidelines, 6 months post guidelines, 1.5 years post guidelines) were identified. The primary measure of interest was the percentage of patients receiving MEC/HEC who were treated in concordance with the updated CINV guidelines. Secondary measures included the percentage of MEC/HEC patients who experienced grade 2+ CINV. The collected data was used with quality improvement techniques to guide the development of interventions to improve guideline concordance. Results: The concordance of anti-emetics on the day of chemotherapy improved over time, but post-chemotherapy concordance remained at 0% (table). The primary driver for concordance was the use of NK1inhibitors on chemotherapy day, and the duration of 5HT3i post-chemotherapy. Using quality improvement methodology, the highest impact intervention was identified as changing the default settings in the computerized order entry system (CPOE) to reflect the updated guidelines. These changes are currently in progress and a test of this change will be presented. Conclusions: Concordance with CINV guidelines improved over time resulting in lower CINV and less need for reactive CINV interventions. Further work to target duration of 5HT3i is ongoing. [Table: see text]
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".