Disease-specific prospective family study cohorts enriched for familial risk
Bibliographic record
Abstract
Most common diseases demonstrate familial aggregation; the ratio of the risk for relatives of affected people to the risk for relatives of unaffected people (the familial risk ratio)) > 1. This implies there are underlying genetic and/or environmental risk factors shared by relatives. The risk gradient across this underlying 'familial risk profile', which can be predicted from family history and measured familial risk factors, is typically strong. Under a multiplicative model, the ratio of the risk for people in the upper 25% of familial risk to the risk for those in the lower 25% (the inter-quartile risk gradient) is an order of magnitude greater than the familial risk ratio. If familial risk ratio = 2 for first-degree relatives, in terms of familial risk profile: (a) people in the upper quartile will be at more than 20 times the risk of those in the lower quartile; and (b) about 90% of disease will occur in people above the median. Historically, therefore, epidemiology has compared cases with controls dissimilar for underlying familial risk profile. Were gene-environment and gene-gene interactions to exist, environmental and genetic effects could be stronger for people with increased familial risk profile. Studies in which controls are better matched to cases for familial risk profile might be more informative, especially if both cases and controls are over-sampled for increased familial risk. Prospective family study cohort (ProF-SC) designs involving people across a range of familial risk profile provide such a resource for epidemiological, genetic, behavioural, psycho-social and health utilisation research. The prospective aspect gives credibility to risk estimates. The familial aspect allows family-based designs, matching for unmeasured factors, adjusting for underlying familial risk profile, and enhanced cohort maintenance.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".