EPID-06. DIAGNOSTIC INTERVAL TIME OF PEDIATRIC CNS TUMORS: A REPORT OF THE CANCER IN YOUNG PEOPLE IN CANADA (CYP-C) DATABASE
Bibliographic record
Abstract
Abstract INTRODUCTION CNS tumors are the second most common neoplasm in children and have historically been associated with longer time to diagnosis. Data on the time-to-diagnosis for Canadian children with CNS tumors are limited and outdated. We aimed at evaluating the diagnostic interval time(DIT) for Canadian children, and identifying factors possibly associated with prolonged DIT. METHODS Using the CYP-C database, we analyzed data from children <15 years, diagnosed with CNS tumors between 2001–2015. DIT was defined as time in weeks, elapsed from the first contact with a healthcare provider to confirming diagnosis. We described DIT according to patient’s demographics, socioeconomic, geographic factors as well as tumor-related criteria. RESULTS Patients from all Canadian provinces, except Ontario, had available timepoints to calculate DIT. The cohort included 842 patients. Mean DIT for all patients was 11.7 weeks(median 1.4). Gliomas had the longest mean DIT and embryonal tumors had the shortest(14.6 and 3.6 weeks p<0.01). ATRT and medulloblastoma had a mean DIT of 1.3 and 4.3 weeks respectively. DIT for HGG was shorter than for LGG (6.4 versus 16.1 weeks, p<0.01). Metastatic disease, infratentorial tumors, or age £36 months had significantly shorter DIT (5.6 vs 12.4 vs 18.4, 7.4 vs 13.1 and 8.6). Sex, annual income(QAIPPE), and distance from tertiary center did not influence DIT. CONCLUSION The current diagnostic interval time for pediatric CNS tumors in Canada is 11.7 weeks(median 1.4weeks). These results only reflect the healthcare system’s contribution toward diagnosis confirmation, but not the patient interval before seeking medical attention.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".