The validity of the family history screen for assessing family history of mental disorders
Bibliographic record
Abstract
There is a need to collect psychiatric family history information quickly and economically (e.g., for genome-wide studies and primary care practice). We sought to evaluate the validity of family history reports using a brief screening instrument, the Family History Screen (FHS). We assessed the validity of parents' reports of seven psychiatric disorders in their adult children probands from the Dunedin Study (n = 959, 52% male), using the proband's diagnosis as the criterion outcome. We also investigated whether there were informant characteristics that enhanced accuracy of reporting or were associated with reporting biases. Using reports from multiple informants, we obtained sensitivities ranging from 31.7% (alcohol dependence) to 60.0% (conduct disorder) and specificities ranging from 76.0% (major depressive episode) to 97.1% (suicide attempt). There was little evidence that any informant characteristics enhanced accuracy of reporting. However, three reporting biases were found: the probability of reporting disorder in the proband was greater for informants with versus without a disorder, for female versus male informants, and for younger versus older informants. We conclude that the FHS is as valid as other family history instruments (e.g., the FH-RDC, FISC), and its brief administration time makes it a cost-effective method for collecting family history data. To avoid biasing results, researchers who aim to compare groups in terms of their family history should ensure that the informants reporting on these groups do not differ in terms of age, sex or personal history of disorder.
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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 | 0.030 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".