Characterization of facial phenotypes of children with congenital hypopituitarism and their parents: A matched case‐control study
Bibliographic record
Abstract
Congenital Hypopituitarism (CH) has traditionally been associated with specific facial phenotypes subsumed under the term midface retrusion, based on cephalometric studies. In this study, we used a systematic anthropometric approach to facial morphology in 37 individuals with CH and their parents, primarily of French Canadian ancestry, and compared them to a control group of 78 French Canadian patients with well-controlled type 1 diabetes and their parents. We were able to demonstrate clear morphological differences, which were more prevalent in the affected group than in the control group. More specifically, we showed the presence of a shorter skull base width (P < 0.001) and reduced inner canthal distance (P = 0.006) in the CH face, as well as a relative underdevelopment of the mandible (P = 0.001). These findings were present in individuals of all ages, and were independent of the duration of growth hormone treatment (median treatment 90.8 months; range 7.2-175.8 months). In addition, skull base width was significantly reduced in both mothers and fathers of affected children compared to the parents of the controls (P < 0.001), despite comparable parental heights, supporting an underlying genetic etiology. Such extensive phenotypic studies have not been done in congenital hypopituitarism and will provide further opportunities for data mining.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".