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Record W2106248063 · doi:10.1093/aje/kwj118

Socioeconomic Status and Childhood Solid Tumor and Lymphoma Incidence in Canada

2006· article· en· W2106248063 on OpenAlexaboutno aff
Gábor Mezei, Marilyn J. Borugian, John J. Spinelli, Russell Wilkins, Zenaida Abanto, Mary L. McBride

Bibliographic record

VenueAmerican Journal of Epidemiology · 2006
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsSocioeconomic statusIncidence (geometry)MedicineEnvironmental healthEpidemiologyDemographyChildhood leukaemiaPediatricsInternal medicineSociologyPopulationMathematics

Abstract

fetched live from OpenAlex

The authors examined the relation between neighborhood income, as a measure of socioeconomic status, and childhood cancer. Incident cases of childhood solid tumor and lymphoma in 1985-2001 were identified from provincial cancer registries in Canada. Residential postal codes at the time of diagnosis were used to assign cases to census neighborhoods. Person-years at risk were determined from quintiles of population by neighborhood income, sex, and 5-year age group, constructed using census population data. Poisson regression was used to calculate incidence rate ratios across neighborhood income quintiles. Compared with the incidence rate in the richest income quintile, moderately lower rate ratios of 0.73 (95% confidence interval: 0.63, 0.86) and 0.84 (95% confidence interval: 0.69, 1.04) were observed, respectively, for carcinomas and renal tumors in the poorest income quintile. No association was found for other types of cancer. Although a potential relation between socioeconomic status and childhood cancer cannot be excluded, the overall pattern seems compatible with random variation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.299
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2006
Admission routes1
Has abstractyes

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