How Different Is Cancer Control across Canada? Comparing Performance Indicators for Prevention, Screening, Diagnosis, and Treatment
Bibliographic record
Abstract
Meaningful performance measures are an important part of the toolkit for health system improvement. The Canadian Partnership Against Cancer has been reporting on pan-Canadian cancer system performance indicators since 2009-work that has led to the availability of standardized measures that can help to shed light on the extent of variation and opportunities for quality improvement across the country. Those measures include a core set of system indicators ranging from prevention and screening, through diagnosis and treatment, to survivorship and end-of-life care. Key indicators were calculated and graphed, showing the range from worst to best result for the provinces and territories included in the data. There were often significant differences in cancer system performance between provinces and territories. For example, smoking prevalence rates ranged from 14% to 62%. The 90th percentile wait times from an abnormal breast screen to resolution (without biopsy) ranged from 4 weeks to 8 weeks. The percentage of breast cancer resections that used breast-conserving surgery rather than mastectomy ranged from 38% to 75%. Clinical trial participation rates for adults ranged from 0.2% to 6.6%. Variations in performance indicators between Canadian jurisdictions suggest potential differences in the planning and delivery of cancer control services and in clinical practice patterns and patient outcomes. Understanding sources of variation can help to identify opportunities for improvements in the quality and outcomes of cancer control service delivery in each province and territory.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 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".