Quantifying diagnostic intervals and routes to diagnosis for children and young people with cancer in the UK (Childhood Cancer Diagnosis study, CCD): a population-based observational study
Bibliographic record
Abstract
Background: Childhood cancer is a global disease burden, with early diagnosis a priority. We quantified diagnostic intervals and referral routes for children and young people (CYP 0-18 years) diagnosed with cancer in the UK. Methods: All CYP diagnosed between September 2020-March 2023 were eligible. Demographic, referral, and symptom data were collected prospectively. Patient interval (PI), diagnostic interval (DI), and total diagnostic interval (TDI) were calculated. Findings: 1957 CYP (mean age 7.4 years, 55% male, 78% white) participated. Median PI, DI, and TDI were 1.1 (IQR 0.1-4.0; range 0-164), 1.7 (IQR 0.4-5.9; range 0-310), and 4.6 weeks (IQR 2.0-11.4; range 0-310), respectively. Intervals were unaffected by sex, ethnicity or deprivation index (IMD). Median TDI was longest in 15-18 years (8.7 weeks, IQR 3.0-17.4) and bone tumours (12.6 weeks, IQR 6.6-23.4) and shortest in under ones (3.7 weeks, IQR 1.0-8.1) and renal tumours (2.3 weeks, IQR 0.9-5.0). 74% (n = 1438) had 1-3 pre-diagnostic healthcare contacts; 67% (n = 1312) presented emergently, with a median of 4.0 (range 0-26) symptoms. CYP with Langerhans Cell Histiocytosis were most likely to have ≥4 visits when compared with leukaemia (adjusted OR 7.48, 95% CI 3.54-15.82), followed by central nervous system, bone, and soft tissue tumours. Interpretation: This study highlights equal access to diagnosis for sex, ethnicity and IMD, but disparities for age and diagnostic groups. These data will inform professional and public health strategies and health policy to accelerate diagnosis for all. Funding: National Institute for Health and Social Care Research (NIHR) DRF-2018-11-ST2-055.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".