Schinzel–Giedion syndrome: Report of splenopancreatic fusion and proposed diagnostic criteria
Bibliographic record
Abstract
We report on the 46th patient with Schinzel-Giedion syndrome (SGS) and the first observation of splenopancreatic fusion in this syndrome. In the antenatal period, a male fetus was found to have bilateral hydronephrosis. Postnatally, in keeping with a diagnosis of SGS, there were large fontanelles, ocular hypertelorism, a wide, broad forehead, midface retraction, a short, upturned nose, macroglossia, and a short neck. Other anomalies included cardiac defects, widened and dense long bone cortices, cerebral ventriculomegaly, and abnormal fundi. Splenopancreatic fusion, usually encountered in trisomy 13, was found on autopsy. Schinzel-Giedion syndrome is likely a monogenic condition for which neither the heritability pattern nor pathogenesis has yet been determined. A clinical diagnosis may be made by identifying the facial phenotype, including prominent forehead, midface retraction, and short, upturned nose, plus one of either of the two other major distinguishing features: typical skeletal abnormalities or hydronephrosis. Typical skeletal anomalies include a sclerotic skull base, wide supraoccipital-exoccipital synchondrosis, increased cortical density or thickness, and broad ribs. Other highly supportive features include neuroepithelial tumors (found in 17%), hypertrichosis, and brain abnormalities. Severe developmental delay and poor survival are constant features in reported patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".