Bibliographic record
Abstract
Recall bias is a major concern in case-control studies in which questionnaire data are used to assess past exposure. The authors conducted a validation substudy within the framework of a parent case-control study on risk factors for acute lymphoblastic leukemia in children aged < or =9 years diagnosed in 1980-1993 in Quebec, Canada. Parental recall bias for two variables was assessed: reported distance from home to power lines compared with measured distance and reported prenatal radiographic examinations compared with hospital medical record data. For reported distance, sensitivity was 62% for a subgroup of cases living in an area in which an excess of childhood acute lymphoblastic leukemia was perceived and was attributed to power lines. However, for other cases, sensitivity (35%) was similar to that measured for hospital controls (36%) and was relatively close to that for population controls (22%). Specificity was high for all groups except cases from the area with a perceived excess. Sensitivity for reported prenatal radiographic examinations was similar for cases (64%) and population controls (71%) but was lower for hospital controls (50%). Results confirm that under special circumstances, such as those resulting from enhanced public concern, parental recall can be differential but otherwise is most often nondifferential, with low sensitivity. Choosing the best type of controls to ensure comparable recall accuracy remains difficult.
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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 | 0.212 | 0.664 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".