Human Phenotype Ontology Annotations for Rare Congenital Conditions: Application to Arthrogryposis Multiplex Congenita
Bibliographic record
Abstract
Arthrogryposis multiplex congenita (AMC) represents a large, rare group of congenital conditions. This study addressed major challenges in AMC research posed by the lack of systematic frameworks for data collection and the use of inconsistent terminologies and text descriptions. We aimed to systematically review the Human Phenotype Ontology (HPO) terms, encode AMC phenotypic traits as HPO terms, and pilot test the encoding process in a cohort of children with AMC. An international consensus-based dataset for AMC was used to extract phenotypic traits from the fetal period to adulthood. The encoding process was developed by an international expert panel to expand and revise HPO ontology for joint contractures, as the main characterizing traits in AMC. Using a pre-tested mapping algorithm, the HPO mapping process resulted in a 62% complete match, a 12% incomplete match, and a 26% no match. The encoding process included 37 new terms and annotations and 13 re-structures across 10 different joints. The implemented annotations significantly increased the number of available HPO terms for joint contractures in a cohort of children with AMC (p-value = 0.04). Our encoding and annotation approach may be used as a blueprint for systematic HPO (re)annotations for musculoskeletal and non-musculoskeletal phenotypic traits of AMC.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".