Facial diagnosis of mild and variant CdLS: Insights from a dysmorphologist survey
Bibliographic record
Abstract
Cornelia de Lange syndrome (CdLS) is a dominant disorder with classic severe forms and milder atypical variants. Central to making the diagnosis is identification of diagnostic facial features. With the recognition that patients with SMC1A and SMC3 mutations have milder, atypical features, we surveyed 65 dysmorphologists using facial photographs from 32 CdLS patients with the goals of (1) Illustrating examples of milder patients with SMC1A mutations and (2) Obtaining objective data to determine which facial features were useful and misleading in making a diagnosis of CdLS. Clinicians were surveyed whether the patient had CdLS or another diagnosis, the certainty of response and the clinical features used to support each response. Using only facial photographs, an average of 24 cases (75%) were accurately diagnosed per clinician. Correct diagnoses were made in 90% of classic CdLS and 87% of non-CdLS cases, however, only 54% of mild or variant CdLS were correctly diagnosed by respondents. We confirmed that CdLS is most accurately diagnosed in childhood and the diagnosis becomes increasingly difficult with age. This survey demonstrated that emphasis is placed on the eyebrows, nasal features, prominent upper lip and micrognathia. In addition, the presence of fuller, atypical eyebrows, a prominent nasal bridge and significant prognathism with age dissuaded survey takers from arriving at a diagnosis of CdLS in individuals with mild NIPBL and SMC1A mutations. This work underscores the difficulty in diagnosing patients with mild and variant CdLS and serves to objectively classify both useful and misleading features in the diagnosis of CdLS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".