Psychological consequences and predictors of adverse events in the first 5 years after predictive testing for Huntington's disease
Bibliographic record
Abstract
The promise of genetic medicine is to provide information, based on genotype, to persons not yet sick about their risk of future illness. However, little is known of the long-term psychological effects for asymptomatic persons learning their risk of having a serious disease. Predictive genetic testing for Huntington's disease (HD) has been offered for the longest time for any disease. In the present study, the psychological consequences of predictive testing were assessed prospectively in individuals at risk for HD during seven visits over 5 years. Questionnaires of standard measures of psychological distress (the General Severity Index of the Symptom Check List-90-Revised), depression (the Beck Depression Inventory), and general well-being (the General Well-Being Scale) were administered to the participants. A significant reduction in psychological distress was observed for both result groups throughout 2 years (p < 0.001) and at 5 years (p = 0.002). Despite the overall improvement of the psychological well-being, 6.9% (14 of 202) of the participants experienced an adverse event during the first 2 years after predictive testing that was clinically significant. The frequency of all defined adverse events in the participants was 21.8%, with higher frequency in the increased risk group (p = 0.03) and most occurring within 12 months of receiving results.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".