Lessons learned from a child with a chromosomal abnormality but no major congenital anomalies
Bibliographic record
Abstract
A 9-year-old female was referred to Genetics with the possible dual diagnoses of 3p deletion and 9p duplication syndromes based on a chromosomal microarray (CMA) report of an unbalanced chromosomal translocation resulting in 3p deletion and 9p duplication. She was the first child born to non-consanguineous German parents with an unremarkable family history. Her mother had well-controlled type 1 diabetes throughout pregnancy. Our patient was delivered at 38 weeks via caesarean section with a birth weight of 3.28 kg. The perinatal course was otherwise unremarkable. By 7 years of life, she had a history of poor growth due to feeding difficulties. The CMA was requested by her paediatrician as part of initial blood tests to investigate her poor growth. All other tests were normal. She underwent adenoidectomy after which her growth parameters improved. Her CMA showed a 4 Mb terminal loss of 3p26.3–p26.1 which included the four OMIM morbid genes (CHL1, CNTN6, CNTN4, and CRBN) implicated in the characteristic features of 3p deletion. In addition, there was a 4.4 Mb 9p24.1–p24.3 duplication, previously reported in individuals with autism and cognitive delays.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".