Intravenous chemotherapy preparation errors: Patient safety risks identified in a pan-Canadian exploratory study
Bibliographic record
Abstract
BACKGROUND: This exploratory study was launched following a critical chemotherapy medication incident to thoroughly and proactively examine the current processes for ordering, preparing, labeling, verifying, administering, and documenting ambulatory intravenous chemotherapy in Canada, and to identify factors that may contribute to preventable adverse drug events. METHODS: Field observations in six Canadian cancer centers to identify end-to-end processes in clinic, pharmacy, and treatment areas; analysis of processes to identify risks. RESULTS: Three types of previously locally unrecognized potential chemotherapy preparation errors in Canadian oncology pharmacies were uncovered, all of which are undetectable if they occur. Although the frequency of these errors is unknown, their impact is potentially catastrophic. INTERPRETATION: Dispensing errors in high-risk intravenous preparation have been studied in the past, but it is unlikely that these studies have detected these errors because of the inherent limitations of the detection methods used. Research on preparation errors using more sensitive methods is therefore urgently needed to establish the extent to which pharmacy preparation practices may be error-prone, and to allow reliable evaluation of the impact of mitigation strategies. Widespread practice changes in Canadian oncology pharmacies are necessary, and are currently underway.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".