The State of Technology in Craniosynostosis
Bibliographic record
Abstract
INTRODUCTION: Craniosynostosis, the premature fusion of ≥1 cranial sutures, is the leading cause of pediatric skull deformities, affecting 1 of every 2000 to 2500 live births worldwide. Technologies used for the management of craniofacial conditions, specifically in craniosynostosis, have been advancing dramatically. This article highlights the most recent technological advances in craniosynostosis surgery through a systematic review of the literature. METHODS: A systematic electronic search was performed using the PubMed database. Search terms used were "craniosynostosis" AND "technology" OR "innovation" OR "novel.' Two independent reviewers subsequently reviewed the resultant articles based on strict inclusion and exclusion criteria. Selected manuscripts deemed novel by the senior authors were grouped by procedure categories. RESULTS: Following review of the PubMed database, 28 of 536 articles were retained. Of the 28 articles, 20 articles consisting of 21 technologies were deemed as being novel by the senior authors. The technologies were categorized as diagnostic imaging (n = 6), surgical planning (n = 4), cranial vault evaluation (n = 4), machine learning (n = 3), ultrasound pinning (n = 3), and near-infrared spectroscopy (n = 1). CONCLUSION: Multiple technological advances have impacted the treatment of craniosynostosis. These innovations include improvement in diagnosis and objective measurement of craniosynostosis, preoperative planning, intraoperative procedures, communication between both surgeons and patients, and surgical education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".