Nonparticipation Selection Bias in the MOBI-Kids Study
Bibliographic record
Abstract
BACKGROUND: MOBI-Kids is a 14-country case-control study designed to investigate the potential effects of electromagnetic field exposure from mobile telecommunications devices on brain tumor risk in children and young adults conducted from 2010 to 2016. This work describes differences in cellular telephone use and personal characteristics among interviewed participants and refusers responding to a brief nonrespondent questionnaire. It also assesses the potential impact of nonparticipation selection bias on study findings. METHODS: We compared nonrespondent questionnaires completed by 77 cases and 498 control refusers with responses from 683 interviewed cases and 1501 controls (suspected appendicitis patients) in six countries (France, Germany, Israel, Italy, Japan, and Spain). We derived selection bias factors and estimated inverse probability of selection weights for use in analysis of MOBI-Kids data. RESULTS: The prevalence of ever-regular use was somewhat higher among interviewed participants than nonrespondent questionnaire respondents 10-14 years of age (68% vs. 62% controls, 63% vs. 48% cases); in those 20-24 years, the prevalence was ≥97%. Interviewed controls and cases in the 15- to 19- and 20- to 24-year-old age groups were more likely to have a time since start of use of 5+ years. Selection bias factors generally indicated a small underestimation in cellular telephone odds ratios (ORs) ranging from 0.96 to 0.97 for ever-regular use and 0.92 to 0.94 for time since start of use (5+ years), but varied in alternative hypothetical scenarios considered. CONCLUSIONS: Although limited by small numbers of nonrespondent questionnaire respondents, findings generally indicated a small underestimation in cellular telephone ORs due to selective nonparticipation.
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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 | 0.063 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".