Generating Advancements in Longitudinal Analysis in X and Y Variations: Rationale, Methods, and Diagnostic Characteristics for the <scp>GALAXY</scp> Registry
Bibliographic record
Abstract
Sex chromosome aneuploidies (SCAs) are a family of genetic disorders that result from an atypical number of X and/or Y chromosomes. SCAs are the most common chromosomal abnormality, affecting ~1/400 live births, yet are often underdiagnosed, leading to over-representation of more severely impacted individuals in many clinical studies. In addition to this ascertainment bias, existing work in SCAs has also been limited by low geographic and demographic diversity. To address these limitations, we have created the Generating Advancements with Longitudinal Analysis in X and Y variations (GALAXY) Registry. Through prioritizing sustainability, transparency, and minimizing participant burden, the overarching goal of the GALAXY Registry is to improve health outcomes for individuals with SCAs by serving as an infrastructure for future SCA research based on a large, heterogeneous, and longitudinal sample. To date, GALAXY has accrued 335 verified SCA participants with an average accrual of 11.2 participants/month (6.7 47,XXY, 1.9 47,XXX, 2.0 47,XYY, 3.2 48,XXYY, 1.8 48,XXXY, and 1.3 Other). Demographic data between those identified to have SCA prenatally (predominantly cell-free DNA screening) differ from those diagnosed postnatally for insurance status, age at enrollment, genetic test type, and reason for SCA diagnosis. Next steps include targeted recruitment of underrepresented groups (e.g., non-47,XXY karyotypes, older adults, minoritized individuals), extraction of medical record data into the registry, international expansion, and continued engagement with the SCA community. As a collaboration between clinician investigators and the SCA community, the GALAXY Registry is a powerful resource for future patient-centered clinical research.
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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 | 0.232 | 0.347 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".