The face of Noonan syndrome: Does phenotype predict genotype
Bibliographic record
Abstract
The facial photographs of 81 individuals with Noonan syndrome, from infancy to adulthood, have been evaluated by two dysmorphologists (JA and MZ), each of whom has considerable experience with disorders of the Ras/MAPK pathway. Thirty-two of this cohort have PTPN11 mutations, 21 SOS1 mutations, 11 RAF1 mutations, and 17 KRAS mutations. The facial appearance of each person was judged to be typical of Noonan syndrome or atypical. In each gene category both typical and unusual faces were found. We determined that some individuals with mutations in the most commonly affected gene, PTPN11, which is correlated with the cardinal physical features, may have a quite atypical face. Conversely, some individuals with KRAS mutations, which may be associated with a less characteristic intellectual phenotype and a resemblance to Costello and cardio-facio-cutaneous syndromes, can have a very typical face. Thus, the facial phenotype, alone, is insufficient to predict the genotype, but certain facial features may facilitate an educated guess in some cases.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".