Replication Data and Code for: Inequalities in relative cancer survival by race, immigration status, income, and education for 22 cancer sites in Canada, a cohort study
Bibliographic record
Abstract
The following files are code and partial data to reproduce results in the study: Malagón T, Botting-Provost S, Moore A, El-Zein M, Franco EL. Inequalities in relative cancer survival by race, immigration status, income, and education for 22 cancer sites in Canada, a cohort study. Int J Cancer. 2025 Jul 1;157(1):41-54. doi: 10.1002/ijc.35337. Epub 2025 Jan 16. PMID: 39821788; PMCID: PMC12062929. https://doi.org/10.1002/ijc.35337 The SAS programs format the data from original microdata files and perform the propensity score matching and Poisson regression models. The outputs from the Poisson regression models were saved as Excel files, and were used to calculate relative survival using the R program "relative survival from propensity score models appendix version.R". The partial data of Poisson model outputs can be used to generate custom estimates of survival and mortality rates by race, immigration status, household income, and education level using estimating equations based on fitted Poisson model parameters. Additional SAS programs used for the secondary analyses using life table methods are also included here. The microdata files used to fit the models and other results of the study are owned by Statistics Canada; restrictions apply to the availability of these data, which were used under license by Statistics Canada for this study. Eligible researchers can apply for access to this data through the Statistics Canada Research Data Centre program (https://www.statcan.gc.ca/en/microdata/data-centres/access).
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 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 | 0.009 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.290 | 0.073 |
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".