The Role of Preoperative Computed Tomography in Nonsyndromic Craniosynostosis
Bibliographic record
Abstract
BACKGROUND: There is currently no consensus on the utility of preoperative computed tomography (CT) in nonsyndromic craniosynostosis. This systematic review and meta-analysis examines the evidence available on the necessity of preoperative CT for the treatment of nonsyndromic craniosynostosis. METHODS: A comprehensive literature review of the National Library of Medicine (PubMed) database was performed. The following variables were analyzed: concordance of findings between clinical examinations and CT, incidental findings reported on imaging, and the effect of imaging on subsequent management. Concordance between clinical examination/CT and the presence of incidental findings were collected and displayed as descriptive data. The effect of imaging on subsequent diagnosis/management was analyzed by meta-analysis. RESULTS: Eleven studies met the inclusion criteria for a total of 728 patients. Overall, physical examination concordance with CT diagnosis was 97.9% (371/379). Overall, incidental findings led to additional imaging/workup in 1.79% of cases (5/278). The results of the meta-analysis revealed that, in the absence of alternative imaging modalities, CT scans significantly altered diagnosis or led to additional investigations in 12 cases (4.8%, 95% confidence interval = 3%-8%). Preoperative CT scans led to additional investigations in 5 cases and detected incomplete/wrong diagnoses in 7 cases. CONCLUSIONS: The results of the present meta-analysis support the use of preoperative CT scans for nonsyndromic craniosynostosis in the absence of alternative imaging modalities. The results also suggest that in properly selected patients, alternative imaging modalities may be appropriate, potentially obviating the need for CT scans.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".