Potential Nonresponse Bias in a Clinical Examination After Initial Screening Using Iron Phenotyping and <i>HFE</i> Genotyping in the Hemochromatosis and Iron Overload Screening Study
Bibliographic record
Abstract
BACKGROUND: Little is known about the factors affecting participation in clinical assessments after HEmochromatosis and IRon Overload Screening. METHODS: Initial screening of 101,168 primary care patients in the HEmochromatosis and IRon Overload Screening study was performed using serum iron measures and hemochromatosis gene (HFE) genotyping. Using iron phenotypes and HFE genotypes, we identified 2256 cases and 1232 controls eligible to participate in a clinical examination. To assess the potential for nonresponse bias, we compared the sociodemographic, health status, and attitudinal characteristics of participants and nonparticipants using adjusted odds ratios (ORs) and 95% confidence interval (CI). RESULTS: Overall participation was 74% in cases and 52% in controls; in both groups, participation was highest at a health maintenance organization and lowest among those under 45 years of age (cases: OR = 0.68; 95% CI 0.53, 0.87; controls: OR = 0.59; 95% CI 0.44, 0.78). In controls only, participation was also lower among those over 65 years of age than the reference group aged 46-64 (OR = 0.64; 95% CI 0.47, 0.88). Among cases, participation was higher in HFE C282Y homozygotes (OR = 3.98; 95% CI 2.60, 6.09), H63D homozygotes (OR = 2.79; 95% CI 1.23, 6.32), and C282Y/H63D compound heterozygotes (OR = 1.82; 95% CI 1.03, 3.22) than in other genotypes, and lower among non-Caucasians and those who preferred a non-English language than in Caucasians and those who preferred English (p < 0.0001). CONCLUSIONS: Subjects with greatest risk to have iron overload (C282Y homozygotes; cases > or =45 years; Caucasians) were more likely to participate in a postscreening clinical examination than other subjects. We detected no evidence of strong selection bias.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".