Waiting times for cancer care in Canadian children: Impact of distance, clinical, and demographic factors
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to evaluate the impact of distance from residence to pediatric oncology centers on waiting times to diagnostic assessment and treatment in Canadian children with cancer. PROCEDURE: Two thousand three hundred sixteen children (< 15 years of age) captured by the Canadian Childhood Cancer Surveillance and Control Program's (CCCSCP) Treatment and Outcome Surveillance (TOS) system were included in the analysis. The number of days between onset of symptoms and first anti-cancer therapy was compared for those living less than 25, 25-99, and more than 100 kilometers from their treating center. For each time period, the adjusted odds of falling into the longest time quartiles were estimated using logistic regression analysis. RESULTS: Thirty-four percent (n = 786) of children lived more than 100 kilometers from their treating center. There were no significant differences in age at diagnosis (P = 0.33), sex (P = 0.68), or ICCC diagnosis (P = 0.02) by distance from center, though there was a significant difference in region of residence (P < 0.01) and the first health care professional contacted (P < 0.01). Except for waiting time from first health care contact to first assessment by treating oncologist which increased with increasing distance (P < 0.01), there were no significant differences in waiting times by distance. When adjusted for important demographic and clinical characteristics, distance to center did not affect the odds of waiting longer for any event. CONCLUSIONS: Distance to treating center had no significant impact on waiting times for important diagnostic and treatment events, when adjusted for age at diagnosis, diagnosis, region, and first health care professional seen.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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, 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".