Anticipation of a therapeutic odyssey following predictive testing for autism
Bibliographic record
Abstract
Brain-based tools are being developed to identify infants at ultra-high likelihood for developing autism and enable presymptomatic intervention, though such interventions are not yet clinically available. Given persistent challenges in accessing autism services, we sought to understand how families might use early predictive results to seek support. We analyzed 55 interviews with parents of infants aged 6-13 months; one group had experience parenting an older autistic child (n = 30), the other had no prior autism parenting experience (n = 25). All parents were asked what steps they would take if told their infant was likely to develop autism. Both groups described an intent to find appropriate services; parents with prior autism experience provided more specifics based on prior knowledge. The groups diverged in their anticipated supports and information sources. Parents with autism experience anticipated seeking financial support via insurance and disability benefits; those without autism experience reported they would consult their pediatrician for information or search online. This qualitative study was conducted with a sample of parents selected for their specific life experiences, but likely does not capture the full range of potential responses to biomarker testing in infancy. Given that most services and benefits require a formal diagnosis, families receiving predictive results in infancy will likely face challenges finding appropriate services. Prior to implementing predictive testing in the first year of life, researchers should consider their obligation to support families who receive predictive 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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".