Clinical Significance of Venous Anomalies in Syndromic Craniosynostosis
Bibliographic record
Abstract
Background: The pattern of cranial venous drainage in syndromic craniosynostosis is unpredictable and not adequately understood. Collateral channels substitute for stenotic venous sinuses and pose potential risk for surgical intervention. The purpose of this study was to analyze the patterns of venous drainage in patients with syndromic craniosynostosis and their influence on operative planning and morbidity. Methods: A retrospective study of patients with syndromic craniosynostosis from 2000 to 2013 was performed. Demographic data were collected including phenotype and associated pathologies. Pre- and/or postoperative venous imaging was reviewed for venous sinus stenosis, collateral emissaries, and persistent fetal sinuses. Categorization of anomalous venous drainage was performed, and the relationship with surgical morbidity was assessed. Results: Forty-one patients were identified. Anomalies were present in 31 patients (76%) consisting of dural sinus stenosis in 28 (68%), dilated emissaries in 26 (63%), and fetal sinuses in 7 (17%). Pfeiffer syndrome was most commonly associated with anomalous drainage (100%). Venous anomalies were associated with elevated intracranial pressure (ICP), shunted hydrocephalus, Chiari malformations, and sleep apnea. In 5 cases, the surgical plan was adjusted based on anomalous anatomy. No mortalities occurred. Intraoperative complication rate was 7.3%, all with anomalous drainage. Median estimated blood loss was 1,100 cc for patients with anomalies versus 400 cc without anomalies (P = 0.181). Conclusion: Cranial venous anomalies are commonly detected in patients with syndromic craniosynostosis and may affect surgical morbidity and outcome with a higher estimated blood loss, alteration of procedure, and postoperative morbidity. Detailed preoperative imaging of the venous drainage is therefore recommended in cases of syndromic synostosis.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".