Optimization of Cranio-Orbital Remodeling
Bibliographic record
Abstract
Cranio-orbital remodeling aims to correct the dysmorphic skull associated with craniosynostosis. Traditionally, the skull is reconstructed into a shape that is subjectively normal according to the surgeon's perception. We present a novel technique using a mathematical algorithm to define the optimal location for bony osteotomies and to objectively reshape the fronto-orbital bar into an ideal normal skull contour. Using pre-operative computed tomography images, the abnormal skull contour at the frontal-orbital region was obtained for infants planned to undergo cranio-orbital remodeling. The ideal skull shape was derived from an age- and sex-matched normative skull library. For each patient, the mathematical technique of dynamic programming (DP) was applied to compare the abnormal and ideal skull shapes. The DP algorithm identifies the optimal location of osteotomy sites and calculates the objective difference in surface area remaining between the normative and dysmorphic skull shape for each solution applied. By selecting the optimal solution with minimal objective difference, the surgeon is guided to reproducibly recreate the normal skull contour with defined osteotomies. The DP algorithm was applied in 13 cases of cranio-orbital remodeling. Five female and 8 male infants with a mean age of 11 months were treated for craniosynostosis classified as metopic (n = 7), unicoronal (n = 4), or bicoronal (n = 2). The mean OR time was 190.2 min (SD 33.6), mean estimated blood loss 244 cc (SD 147.6), and 10 infants required blood transfusions. Compared with a historical crania-orbital remodeling group treated without application of the algorithm, there was no significant difference in OR time, estimated blood loss, or transfusion rate. This novel technique enables the craniofacial surgeon to objectively reshape the fronto-orbital bar and reproducibly reconstruct a skull shape resembling that of normal infants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".