Current state assessment of the organization and delivery of systemic treatment in Ontario.
Bibliographic record
Abstract
20 Background: In 2019, Ontario Health (Cancer Care Ontario) published 54 standards to ensure high quality and safe delivery of systemic treatment (ST) in Ontario, along with 16 recommendations for take-home cancer drugs (THCD). The standards/recommendations focused on 7 domains for the delivery of ST delivery and 8 domains for THCD. These domains varied between the two documents but also overlapped on issues including prescribing, patient care, patient education and training and education for providers. The standards for ST delivery were also prioritized according to Very High, High or Medium priority with regards to implementation expectations. The objective of this project was to obtain a baseline assessment of alignment with the standards/recommendations for all ST sites within Ontario. Methods: A validated electronic survey that linked to all standards/recommendations was distributed to 75 ST sites in August 2019. Sites had 8 weeks to complete the survey with their multi-disciplinary teams. Data was analyzed centrally using quantitative analysis methods by region as well as by site level. Results: The response rate was 100%. Overall, alignment in all domains was higher for intravenous cancer drug (IVCD) delivery as compared to THCD delivery. Important areas of gaps include CPOE/PPO use (75% for both IVCD and THCD); appropriate drug labels (90% for IVCD versus 52% for THCD); prescribing/dispensing independent double checks (IDC) (95% for IVCD versus 38% for THCD); pump independent double check (83% of sites); continuous ST with central line (86% of sites); standardized tool for THCD education (40%); and oncology training/education (96% of RNs versus 20% of pharmacists). Conclusions: The main gaps that were identified through the current state assessment were related to THCD, as opposed to IVCD. To ensure alignment with the standards/recommendations, these gaps should continue to be an area of focus for quality improvement. The survey was instrumental in informing provincial, regional, and local strategies to address these gaps and to ensure high quality, safe practices are embedded in ST delivery as outlined in the published best practice documents.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".