GenIDA, a participatory patient registry for genetic forms of intellectual disability provides detailed caregiver-reported information on 237 individuals with Koolen-de Vries syndrome
Bibliographic record
Abstract
Purpose GenIDA is an international patient registry for individuals diagnosed with intellectual disability, autism spectrum disorder, and/or epilepsy, which is based on an online questionnaire that is completed by parent caregivers. In this study, the GenIDA data on Koolen-de Vries syndrome (KdVS) was analyzed illustrating the value of GenIDA and patient/caregiver participation in rare genetic neurodevelopmental disorders (NDDs). Methods Recruitment was done on the GenIDA website from November 2016 to February 2022. Clinical information on individuals with KdVS was extracted for in-depth analysis and for comparison with the GenIDA data of individuals diagnosed with other NDDs. Results A total of 1417 patients/caregivers across 35 genetic conditions answered to the GenIDA questionnaire, including caregivers of 237 individuals with KdVS. GenIDA findings on KdVS were consistent with the existing literature, and there were no significant differences between individuals with a 17q21.31 microdeletion and those with a pathogenic variant in the KANSL1 gene. GenIDA provided detailed clinical information including features that are over-represented in KdVS compared with other NDDs (eg, laryngomalacia). Modeling of the natural history showed a positive development of speech and language over time and relatively good reading ability in KdVS. Valproate and oxcarbazepine were reported as effective antiepileptic drugs, and responses to open-ended questions indicated that childhood recurrent pneumonia and asthma are clinically relevant comorbidities that were not described in KdVS before. Conclusion GenIDA is a powerful registry to collect and harness valuable data on rare NDDs. The study shows that caregiver-driven data collection is effective in terms of global recruitment and centralization of clinical data.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".