Skeletal phenotype in patients with Shwachman–Diamond syndrome and mutations in <i>SBDS</i>
Bibliographic record
Abstract
Pancreatic exocrine and bone marrow dysfunctions are considered to be universal features of Shwachman-Diamond syndrome (SDS) whereas the associated skeletal dysplasia is variable and not consistently observed. The genetic defect in SDS has recently been identified; causative mutations have been shown in the SBDS gene. The aims of this study were to characterize the nature, frequency, and age-related changes of radiographic skeletal abnormalities in patients with SBDS mutations and to assess genotype-phenotype correlation. Fifteen patients (mean age 9.7 years) with a clinical diagnosis of SDS and documented SBDS gene mutations were included. Review of their skeletal radiographs showed abnormalities in all patients. The skeletal changes were variable, even in patients with identical genotypes. The typical features were (1) delayed appearance of secondary ossification centers, (2) variable widening and irregularity of the metaphyses in early childhood, followed by progressive thickening and irregularity of the growth plates, and (3) generalized osteopenia. There was a tendency towards normalization of the epiphyseal maturation defect and progression of the metaphyseal changes with age. The results suggest that the characteristic skeletal changes are present in all patients with SDS and SBDS mutations, but their severity and localization varies with age. No phenotype-genotype correlation was observed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".