The Initiative to Maximize Progress in Adolescent and Young Adult Cancer Therapy (IMPACT) Cohort Study: a population-based cohort of young Canadians with cancer
Bibliographic record
Abstract
BACKGROUND: Cancer is the leading cause of disease-related death in adolescents and young adults (AYA). Annual improvements in AYA cancer survival have been inferior to those observed in children and older adults. Prior studies of AYA with cancer have been limited by their focus on patients from select treatment centres, reducing generalizability, or by being population-based but lacking diagnostic and treatment details. There is a critical need to conduct population-based studies that capture detailed patient, disease, treatment and system-level data on all AYA regardless of treatment location. METHODS/DESIGN: We will create a cohort of all AYA (aged 15-21 years) at the time of diagnosis with any malignancy between 1992 and 2011 in Ontario, Canada (n = 5,394). Subjects will be identified through the Ontario Cancer Registry and the final cohort will be expanded to include 2012 diagnoses, as these data become available. Detailed diagnostic, treatment and outcome data for those patients treated at a pediatric cancer centre will be provided by a population-based pediatric cancer registry (n = 1,030). For 15-18 year olds treated at adult centres (n = 923) and all 19-21 year olds (n = 3396), trained abstractors will collect the comparable data elements from medical records. We will link these data to population-based administrative health data that include physician billings, hospitalizations and emergency room visits. This will allow descriptions of health care access and use prior to cancer diagnosis, and during and after treatment. DISCUSSION: The IMPACT cohort will serve as a platform for addressing questions that span the AYA cancer journey. These will include determining which factors influence where AYA receive care, the impact of locus of care on the types and intensity of cancer therapy, appropriateness of surveillance for disease recurrence, access to clinical trials, and receipt of palliative and survivor care. Findings using the IMPACT cohort have the potential to lead to changes in practice and cancer policy, reduce mortality, and improve quality of life for AYA with cancer. The IMPACT data platform will be a permanent resource, accessible to researchers across Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".