How should we construct psychiatric family history scores? A comparison of alternative approaches from the Dunedin Family Health History Study
Bibliographic record
Abstract
BACKGROUND: There is increased interest in assessing the family history of psychiatric disorders for both genetic research and public health screening. It is unclear how best to combine family history reports into an overall score. We compare the predictive validity of different family history scores. METHOD: Probands from the Dunedin Study (n=981, 51% male) had their family history assessed for nine different conditions. We computed four family history scores for each disorder: (1) a simple dichotomous categorization of whether or not probands had any disordered first-degree relatives; (2) the observed number of disordered first-degree relatives; (3) the proportion of first-degree relatives who are disordered; and (4) Reed's score, which expressed the observed number of disordered first-degree relatives in terms of the number expected given the age and sex of each relative. We compared the strength of association between each family history score and probands' disorder outcome. RESULTS: Each score produced significant family history associations for all disorders. The scores that took account of the number of disordered relatives within families (i.e. the observed, proportion, and Reed's scores) produced significantly stronger associations than the dichotomous score for conduct disorder, alcohol dependence and smoking. Taking account of family size (i.e. using the proportion or Reed's score) produced stronger family history associations depending on the prevalence of the disorder among family members. CONCLUSIONS: Dichotomous family history scores can be improved upon by considering the number of disordered relatives in a family and the population prevalence of the disorder.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".