The Answer ALS return of results study: Answering the duty to disclose
Bibliographic record
Abstract
Objective: The Return of Answer ALS Results (RoAR) Study was designed to provide a mechanism for participants in Answer ALS, a large, prospectively designed natural history and biorepository study to receive select clinical genetic testing results and study participants’ experience with the results disclosure. Methods: Participants consented to receive results of five ALS genes (C9orf72, SOD1, FUS, TARDP, TBK1) and/or 59 medically actionable genes as designated by the American College of Medical Genetics. Patient-reported genetic testing outcomes were measured via a post-disclosure survey. Results: Of 645 eligible Answer ALS enrollees, 143 (22%) enrolled and completed participation in RoAR. Pathogenic variants were identified in 22/143 (15.4%) participants, including 13/143 (9.0%) in ALS genes and 9/143 (6.3%) in ACMG genes. Participant-reported measures of result utility indicated the research result disclosure was as or more successful than published patient-reported outcomes of result disclosure the clinical setting. Conclusions: This study serves as a model of a “disclosure study” to share results from genomic research with participants who were not initially offered the option to receive results, and our findings can inform the design of future, large scale genomic projects to empower research participants to access their genetic information.
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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 | 0.040 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".