Improving the safe delivery of systemic treatment by assessing concordance with labeling guidelines.
Bibliographic record
Abstract
257 Background: Improper labeling of medication may lead to errors. In 2009, Cancer Care Ontario published Key Components of Chemotherapy Labeling, with recommendations for the necessary components and formatting of intravenous chemotherapy labels. A jurisdiction-wide evaluation for concordance occurred in 2011. Results were shared, improvement efforts were supported through a provincial quality network, and a re-evaluation was conducted in the fall of 2013. Methods: Three defined chemotherapy labels were evaluated at baseline and after improvement strategies were implemented at each of Ontario’s 77 hospitals providing systemic treatment. Labels were reviewed centrally and awarded points for concordance for each of 15 guideline-specified criteria. Results: The provincial average overall score for concordance increased from 59% to 80% (p<0.001). Improvement was seen for 12 of the 15 criteria evaluated and for 64 of the 77 facilities. The greatest increase in overall score by a facility was 53.4%. The greatest overall improvement in score for an individual component was 67% (TALLman lettering). The scores of 2 components were unchanged, as 100% concordance was achieved on both the baseline and re-evaluation. Conclusions: Improvement in concordance to chemotherapy labeling guidelines was observed following the implementation of a measurement strategy and improvement plans. This approach is one component of a larger strategy to promote a culture of safety in chemotherapy delivery in Ontario. [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 | 0.019 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".