Common data elements for arthrogryposis multiplex congenita: An international framework
Bibliographic record
Abstract
AIM: To facilitate multisite studies and international clinical research, this study aimed to identify consensus-based, standardized common data elements (CDEs) for arthrogryposis multiplex congenita (AMC). METHOD: A mixed-methods study comprising of several focus group discussions and three rounds of modified Delphi surveys to achieve consensus using two tiered-rating scales were conducted. RESULTS: Overall, 45 clinical experts and adults with lived experience (including 12 members of an AMC consortium) participated in this study from 11 countries in North America, Europe, and Australia. The CDEs include 321 data elements and 19 standardized measures across various domains from fetal development to adulthood. Data elements pertaining to AMC phenotypic traits were mapped according to the Human Phenotype Ontology. A universal governance structure, local operating protocols, and sustainability plans were identified as the main facilitators, whereas limited capacity for data sharing and the need for a federated informatics infrastructure were the main barriers. INTERPRETATION: Collection of systematic data on AMC using CDEs will allow investigations on etiological pathways, describe epidemiological profile, and establish genotype-phenotype correlations in a standardized manner. The proposed CDEs will facilitate international multidisciplinary collaborations by improving large-scale studies and opportunities for data sharing, knowledge translation, and dissemination. WHAT THIS PAPER ADDS: The common data elements (CDEs) for arthrogryposis multiplex congenita include 321 data elements from perinatal period to adulthood. A universal governance structure, partner-specific operating protocols, and sustainability plans will facilitate multi-institutional implementation of CDEs. Inconsistent electronic infrastructure, different terminologies for databases, and feasibility issues are the main barriers for international implementation of CDEs.
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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 | 0.231 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".