The Impact of Distance and Income on Pediatric Solid Extracranial Tumors: A Report From <scp>CYP</scp> ‐C
Bibliographic record
Abstract
BACKGROUND: The impact of social determinants of health (SDoH) on survival outcomes is unclear in the universal Canadian health care system. We investigated the impact of distance to treatment center and income quintile on survival outcomes in pediatric extracranial solid tumors in Canada. METHODS: Children < 15 years old diagnosed with 7 common solid extracranial tumors from 2001 to 2020 were included using the Cancer in Young People in Canada (CYP-C) data tool. We used logistic regression to examine the association of income quintile and distance on cancer outcomes. We used Cox proportional hazard models to examine associations with time-to-event outcomes (OS) and Fine-Gray competing risk regression (recurrence) adjusting for metastasis, age, region, and tumor location. RESULTS: The cohort included 3969 patients. Median age was 3.6 years (IQR: 1.3-8.9); 48.7% were female. Tumor diagnosis: 34% neuroblastoma, 21% Wilms tumor, 13% rhabdomyosarcoma, 11% osteosarcoma, 8% Ewing sarcoma, 7% hepatoblastoma, and 6% germ cell tumors. On multivariable analysis, income quintile and distance did not significantly or consistently impact survival across all tumors. In rhabdomyosarcoma, the second lowest income quintile had inferior survival compared to the highest income quintile (p = 0.0264, HR 1.91, 95% CI 1.08, 3.37). In neuroblastoma, the lowest income quintile had inferior survival compared to the highest (p = 0.0052, HR 1.82, 95% CI 1.20, 2.77). Patients diagnosed with hepatoblastoma living > 500 km from a pediatric treatment facility had inferior OS compared to those within 50 km (p = 0.0065, HR 3.29, 95% CI 1.40, 7.79). CONCLUSION: Overall, distance and income did not show a consistent significant impact on survival outcomes for children with extracranial solid tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".