Posterior Cranial Distraction in Craniosynostosis: A Systematic Review of the Literature
Bibliographic record
Abstract
OBJECTIVE: Posterior cranial distraction (PCD) is a surgical technique to address craniosynostosis, especially in syndromic patients. The technique has the ability to significantly expand the cranium, while requiring minimal dural dissection, compared to cranial remodeling. Our goals were to determine the patient characteristics and surgical outcomes of PCD. The two questions that we sought to answer were: 1) What is the average published complication rate and the most common complications of PCD? and 2) How much intracranial volume expansion can one expect with PCD? DESIGN: A PubMed database search of articles on PCD was performed. Case reports and articles with overlapping patients were excluded. A systematic review was performed using the remaining articles. MAIN OUTCOME MEASURES: Patient data were extracted in order to determine the total number of patients, patients with a syndrome, types of syndromes, mean age at surgery, mean distraction distance, mean increase in intracranial volume, and complications. RESULTS: . The overall complication rate was 32.2%, with the most common complications being surgical-site infection, hardware-related complications and delayed wound healing. CONCLUSIONS: PCD is a powerful technique in the management of syndromic craniosynostosis, although complication rates are significantly higher than traditional remodeling techniques. Future studies should compare the effects of supratorcular and infratorcular osteotomies on intracranial volume, cosmesis and complications.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".