Socioeconomic Status, Diagnostic Interval Length and Outcome in Pediatric Acute Lymphoblastic Leukemia
Bibliographic record
Abstract
Efforts to determine the causes of treatment failure in acute lymphoblastic leukemia (ALL), the most common childhood cancer, have neglected the potential role of socioeconomic status (SES). This thesis aimed to address three key knowledge gaps in the field of SES and pediatric oncology. First, we evaluated the current literature by conducting a systematic review of studies evaluating the impact of SES upon childhood cancer survival. Of the 36 studies identified, we found that low SES was uniformly associated with inferior outcome in low- and middle-income countries. The same association was commonly found in studies conducted in high-income countries. Secondly, as several researchers have proposed that prolonged diagnostic intervals (time between first healthcare access and diagnosis) may serve as a pathway linking low SES and inferior cancer outcome, we developed and validated a novel measure of diagnostic interval length among Ontario children with ALL using population-based health services data. Using this measure, we found that prolonged diagnostic interval (≥4 days) was associated with having a general practitioner (GP) as primary care physician vs. a pediatrician [adjusted odds ratio (OR) 1.60, 95% confidence interval (CI) 1.04-2.47; p=0.03]. Socioeconomic and healthcare access variables were not associated with interval length. While prolonged diagnostic intervals were associated with superior event-free survival [hazard ratio (HR) 0.71, 95% CI 0.52-0.98; p=0.04], this association was confounded by disease biology. Finally, we aimed to determine the impact of SES upon Ontario children with ALL. We found that while low SES was not associated with inferior EFS, immigrant status was associated with superior outcome (HR 0.36, 95% CI 0.13- 0.96; p=0.04). These findings have important implications for patients, caregivers, researchers and policymakers. Future studies should use the methodologies developed in this thesis to explore the impact of SES and diagnostic interval length in other malignancies
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".