Hospital or Population Controls for Case-Control Studies of Severe Childhood Diseases?
Bibliographic record
Abstract
There are few empirical data to determine which control group seems best in a case-control study for a severe disease: population controls or hospital controls. The author conducted a case-control study of leukemia in children using two control groups, population and hospital controls (cancers other than leukemia and severe blood diseases), between 1980 and 1993 in Québec, Canada. Maternal, paternal, and child factors not known to be associated with leukemia as well as factors possibly associated were selected for analysis. Most factors were taken directly from parental interviews, but two factors related to parental occupational exposures were blindly coded by chemists. Hospital and population controls were compared using odds ratios estimated from logistic regression. Cases were compared with both types of controls with the same statistical method. Prevalence data from ongoing population surveys were compared with reported prevalence in controls. From the former comparisons and the distribution of socioeconomic variables, results suggested that study groups came from the same base population. Nevertheless, reported and coded exposures among hospital controls were closer to those of cases than to those of population controls. Although substantially different for only one factor, inferences using hospital controls in comparison with population controls resulted in odds ratios closer to the null value.
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 | 0.118 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".