Developing a culture of safety in systemic cancer treatment at the system level.
Bibliographic record
Abstract
230 Background: Chemotherapy ordering, preparation and delivery involve multiple providers and complex systems where high impact errors may occur. Cancer Care Ontario, a provincial agency responsible for continually improving cancer services in Ontario, Canada employs a comprehensive systematic approach to build a culture of safety for systemic treatment. Methods: A comprehensive strategy is applied at 77 systemic treatment hospitals in the Province. A multi-pronged approach is used that includes: 1) system planning, organization and funding, 2) engagement of health care providers working at the regional and local levels, 3) guidelines implementation and, 4) quality measurement. Results: Institutions are organized into regional networks, according to four levels of service complexity based on quality standards, planning, funding, coordination and health human resources. A customized, system-wide incident reporting system is available. Quality improvement is undertaken in several ways. Regional clinical and administrative leaders foster engagement with local providers and work is facilitated by a provincial multidisciplinary community of practice, an annual Safety Symposium, and local improvement projects with funding support. In this way, collaborative sharing and learning occurs across the Province. Comprehensive evidence-based guidelines have been produced addressing safe labeling, administration, handling and the use of computerized prescriber order entry systems. Routine performance management together with guideline concordance measurement, public reporting, planning for improvement and re-evaluation strategies, has produced system improvements. Conclusions: A comprehensive, evidence-based and systematically applied approach to providing systemic cancer treatment can produce a culture of safety that is coordinated and standardized across multiple providers and provider sites.
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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 | 0.058 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.004 |
| 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".