Diagnostic and treatment intervals are not associated with survival in rhabdomyosarcoma: A Cancer in Young People in Canada study
Bibliographic record
Abstract
BACKGROUND: Delay in diagnosis and treatment initiation can be associated with adverse outcomes in children with cancer. Diagnostic interval (DI) is defined as the time between the date of first health care contact for symptoms related to cancer to the date of cancer diagnosis, and treatment interval (TI) is defined as interval between the definitive cancer diagnosis and cancer treatment initiation. We aimed to determine the predictors of DI and TI in children with rhabdomyosarcoma (RMS) and their association with event-free survival (EFS) and overall survival (OS). METHODS: Using the Cancer in Young People in Canada (CYP-C) national population-based database, we conducted a retrospective cohort study of children (0-14.99 years) newly diagnosed with RMS between 2001 and 2015 in Canada. Quantile regression was used to assess the predictors of DI and TI, and Cox regression was used to determine if these intervals were associated with EFS and OS. RESULTS: Median DI and TI were 16.5 days (interquartile range [IQR] 6.0-38.0) and 5 days (IQR 0-12), respectively. DI and TI were not significantly associated with age at diagnosis, sex, race, tumor site, stage or histology, treatment region, distance from treatment center, income quintile or diagnosis year (all p > .05). DI and TI were not associated with EFS (DI: hazard ratio [HR] 1.00, 95% CI 0.96-1.05, p = .871; TI: HR 1.03, 95% CI 1.00-1.05, p = .053) or OS (DI: HR 0.99, 95% CI 0.94-1.05, p = .797; TI: HR 1.02, 95% CI 0.99-1.05, p = .155). CONCLUSIONS: In the publicly funded Canadian health care system, DI and TI did not affect the survival of children with RMS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".