Childhood leukaemia and socioeconomic status
Bibliographic record
Abstract
In a recent issue of the journal, we were pleased to see continued interest by Smith and colleagues1 in the relationship between socioeconomic status (SES) and the development of childhood leukaemia. We agree with the authors that the completeness and representativeness of their data as well as the ability to measure deprivation in several ways are strengths of their study. We would like to point out to the authors that although on the surface the methodology of the UK investigation was different from our recent registry-based Canadian study,2 in fact, the studies were similar. The major difference was that our study did not include enhanced surveillance to identify the childhood leukaemia cases. Due to the centralized cancer registration and treatment system in Canada, however, it is likely that our registries do not suffer from the underreporting problems which seem to have been present in the UK cancer registration.3,4 Therefore, it is likely that both studies captured most, if not all, childhood leukaemia cases. The other differences were minor. We used the entire population as our comparison group rather than selecting a subset of the population as a control group, and we included a longer time period (1985–2001 for most provinces) in the analysis. Finally, our study was unable to look at SES at different time points.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".