Impact of Age and Diagnosis on Waiting Times Between Important Healthcare Events Among Children 0 to 19 Years Cared for in Pediatric Units
Bibliographic record
Abstract
BACKGROUND: The objectives were to describe and compare waiting times to diagnosis and treatment of children and adolescents who accessed pediatric oncology centers in Canada for healthcare, and to assess the effects and relative contributions of age, sex, and diagnosis to waiting times. METHODS: Waiting times were assessed for 2,365 children (0 to 14 y) and 375 adolescents (15 to 19 y) diagnosed with cancer between 1995 and 2000 inclusive and followed by the Treatment and Outcome Surveillance system of the Canadian Children's Cancer Surveillance and Control Program. Differences were assessed using the chi2 test, Fisher exact test, and Wilcoxon test statistic. RESULTS: Median waiting times between first assessment by treating oncologist or surgeon and definitive diagnostic procedure, and the subsequent interval to first therapeutic event, were 2 days each. Significant variation existed in both periods when stratified by age and diagnosis but not sex. The most significant differences between age groups were eliminated when stratified by diagnosis. INTERPRETATION: This analysis suggests that once they enter the healthcare system, children and adolescents treated in pediatric centers in Canada experience short waiting times to key diagnostic and treatment events. Differences in wait times between the 2 age groups are not clinically significant and can be attributed to the differences in the types of cancer experienced by adolescents compared with children.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".