Dose banding of chemotherapy doses at the Juravinski Cancer Centre
Bibliographic record
Abstract
6099 Background: As the number and complexity of chemotherapy regimens increase, the demands on pharmacy services to reduce chemotherapy preparation and checking times continues to increase. Dose banding, a system whereby doses of intravenous cytotoxic drugs calculated on an individual basis are rounded up or down to predetermined standard doses (the maximum variation of the adjustment between standard dose and doses constituting each band is 5% or less) was identified as a strategy that could be used to address some of the issues around time pressures to help reduce patient waiting times for treatment. Methods: The project consisted of 3 phases; Phase I - literature review to identify dose banding publications; Phase II - selection of drugs to be banded for the pilot. The two drugs selected were 5FU and leukovorin, and Phase III - Time studies pre-, interim and post dose banding implementation to determine drug dispensing time and patients’ wait time for pharmacy related procedures. This occurred for a 2 week period (10 working days) either prior to implementation (pre- 819 patients studied), 4 days after implementation (interim - 854 patients studied) and 4 weeks after implementation (post - 785 patients studied). Results: Drug dispensing time did not decrease with dose banding (pre- 7.9 min, interim - 7.6 min and post - 9.4 min). However, the average patient wait time decreased after piloting the dose banding project (pre - 31.6 min, interim 23.7 min, and post - 27.8 min). The percentage of doses that were banded were 37.8% in the interim time study and 58.2% in the post time study. Conclusions: Although dose banding did not reduce dispensing time in this study, likely because the preparation for dispensing 5FU and leukovorin syringes is normally very simple and quick, patient’s wait time for pharmacy related procedures did decrease. This was probably due to contributions of other factors in the pharmacy process. A reduction in dispensing time could likely be achieved if more complex regimens were considered for dose banding. Dose banding could be used to increase capacity within the chemotherapy suite on the day of administration. It also allows for a better work schedule and increases efficiencies within the chemotherapy preparation and administration areas. (Sponsored by funds from Cancer Care Ontario) No significant financial relationships to disclose.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".