Childhood Leukemia and Socioeconomic Status in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Leukemia is one of the most common potentially fatal illnesses in children, and its causes are not well understood. Although socioeconomic status (SES) has been related to leukemia in some studies, this apparent association may be attributable to ascertainment or participation bias. This study was undertaken to determine whether there is a difference in incidence of childhood leukemia for different levels of SES, as measured by neighborhood income, in an unselected population case group. METHODS: All cases of childhood leukemia diagnosed in the years 1985-2001 were identified from population-based cancer registries in Canada. Postal codes for the place of residence at diagnosis were used to ascertain the census neighborhoods for cases. We constructed neighborhood-based income quintiles from census population data, and stratified the population at risk by sex and 5-year age groupings. Age-standardized incidence rates and 95% confidence intervals (CIs) were calculated. We used Poisson regression to compare incidence rate ratios (RRs) across income quintiles. RESULTS: A slightly lower relative risk of childhood leukemia was observed in the poorest quintile compared with the richest (RR = 0.87; 95% CI = 0.80-0.95). The lower risk in the poorest quintile was restricted to acute lymphoid leukemia (0.86; 0.78-0.95) and was strengthened slightly by restriction to urban areas (0.83; 0.74-0.93). CONCLUSIONS: This analysis suggests that high SES is a true risk factor for childhood leukemia and that inconsistent results from other studies may be related to differences in case ascertainment or study participation.
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.
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 | 0.001 | 0.001 |
| 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 it