Epidemiology of delays in care of children and adolescents diagnosed with cancer in Canada
Bibliographic record
Abstract
Background: Although rare relative to adult cancers, cancer is still the leading cause of disease-related death in children in developed countries, including Canada. Few studies have specifically examined the epidemiology and public health significance of diagnosis and treatment delays in childhood cancer. This study aimed to investigate the nature of delays in care for children and adolescents with cancer in Canada and to assess the potential impact of such delays on clinical outcomes. Study Design: I conducted a prospective cohort study to investigate the delays of cancer symptoms reporting, diagnosis, and treatment in children between 0-19 years of age in Canada. This study used a database from Health Canada's Treatment and Outcomes component of the Canadian Childhood Cancer Surveillance and Control Program. Methodology: Patients were identified from 17 paediatric cancer centres across Canada. Subjects included in this study were residents of Canada, aged less than 20 years, diagnosed with a malignant tumour and had information on date of first symptoms, diagnosis, treatment and outcome available. Descriptive statistics and regression techniques (linear, logistic and Cox regression) were used as appropriate. I measured the individual impact of patient and provider delays on disease severity and prognosis by using judicious control for potential confounding mechanisms and mediating factors. Study Findings and Significance: By measuring various types of delays in Canada, I found that varying lengths of patient and referral delay, across age groups, types of cancers, and Canadian settings, are the main contributors to diagnosis, HCS and overall delay. Factors relating to the patients, the parents, healthcare and the cancer may all exert different influences on different segments of cancer care. I also found a negative association between diagnosis delay and disease severity for lymphoma and CNS tumour patients. Furthermore, I found that diagnosis and physician delay had a negative effect, while patient delay had a positive effect, on survival for patients diagnosed with CNS tumours. The information provided from this study may form the basis for new effective policies aimed at eliminating obstacles in cancer the diagnostic and care trajectories for Canadian children with cancer and for improving their prognosis.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".