Predictive genetic testing for adult‐onset disorders in minors: a critical analysis of the arguments for and against the 2013 <scp>ACMG</scp> guidelines
Bibliographic record
Abstract
The publication of the ACMG recommendations has reignited the debate over predictive testing for adult-onset disorders in minors. Response has been polarized. With this in mind, we review and critically analyze this debate. First, we identify long-standing inconsistencies between consensus guidelines and clinical practice regarding risk assessment for adult-onset genetic disorders in children using family history and molecular analysis. Second, we discuss the disparate assumptions regarding the nature of whole genome and exome sequencing underlying arguments of both supporters and critics, and the role these assumptions play in the arguments for and against reporting. Third, we suggest that implicit differences regarding the definition of best interests of the child underlie disparate conclusions as to the best interests of children in this context. We conclude by calling for clarity and consensus concerning the central foci of this debate.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".