System Performance Indicators for Adolescent and Young Adult Cancer Care and Control: A Scoping Review
Bibliographic record
Abstract
Adolescents and young adults (AYAs) with cancer represent a unique group with unmet needs. Metrics and quality indicators are important for evaluating AYA cancer care. The purpose of this study is to describe the quality indicators in a Canadian context that are used for AYA (15-39 years of age) cancer care and control. The Arksey and O'Malley methodological framework was applied to undertake a scoping review of the peer-reviewed and gray literature for indicators related to AYA cancer care and control. OVID Medline was searched from January 1995 until April 2018 for English language articles. Inquiries were made to AYA cancer organizations and a Google search conducted to identify unpublished material. Articles were included if they incorporated AYAs and contained cancer care indicators. Data were summarized at the article and indicator level. A total of 610 abstracts were reviewed. Eighty-nine full-text articles and reports were assessed for eligibility, with 19 included in analyses which identified 146 indicators or indicator concepts. Most of the indicators were specific to the AYA age group (65.8%) and dealt with the active care theme (57.5%), almost half focusing on guideline adherence and treatment (26.4%) and multidisciplinary/specialized care (20.7%). Notable deficits in indicators were in fertility, psychosocial care, and prevention. Important progress has been made internationally and within Canada on developing indicators for AYA cancer care and control. However, there is a lack of well-defined AYA-specific cancer care indicators developed through a consensus process.
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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 | 0.151 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.033 | 0.043 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".