Syndromic and Systemic Diagnoses Associated With Isolated Sagittal Synostosis
Bibliographic record
Abstract
Background: Reports of systemic associations in patients with Isolated Sagittal Synostosis (ISS) are sparse. Craniofacial surgeons, and other providers, should be aware that a significant proportion of patients with ISS may have syndromic or systemic involvement. This study investigates the incidence of systemic disease and syndromic diagnosis in a cohort of patients presenting with ISS (ie, patients with sagittal synostosis without other sutural involvement). Methods: This study consists of a retrospective review of patients diagnosed with ISS between 2007 and 2017 at a single institution. Patients were divided according to onset (early <1 year, late >1 year) of ISS. Patient notes were examined for congenital anomalies, systemic conditions, and molecular testing. Only patients with isolated sagittal fusion—meaning, patients with sagittal synostosis and no other sutural involvement—were included. Results: Three hundred seventy-seven patients met the inclusion criteria: systemic conditions were identified in 188/377 (50%) of them. One hundred sixty-one patients with early onset (Group A), and 216 patients with late onset ISS (Group B) were identified. Systemic involvement was identified in 38% of Group A and 60% of Group B, which was statistically significant (P< 0.001). Forty-eight of 377 (13%) of patients had a syndromic diagnosis, and 79% of these were confirmed via genetic testing. Thirty-five percent of patients were diagnosed with central nervous system anomalies and 16% had craniofacial anomalies. Conclusions: Nearly 50% of the patients initially diagnosed with ISS were found to have some form of systemic involvement. This supports affording full pediatric and genetic evaluation with molecular testing to these children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".