A Novel Application of Computer-Aided Design and Manufacturing for Reduction Cranioplasty
Bibliographic record
Abstract
Macrocephaly is a rare presentation of untreated hydrocephalus. In cases where medical management has failed, macrocephaly can be managed with reduction cranioplasty. Traditionally, reduction cranioplasty is highly dependent on intraoperative tailoring of bone segments to obtain the best possible result and involves high risks of associated severe blood loss and mortality. In this clinical report, we describe a patient with hydrocephalus macrocephaly treated with reduction cranioplasty with the innovative use of computer-aided design and manufacturing to reduce intraoperative risks and improve efficiency. We used computer-aided design to plan osteotomy sites and the final positioning of bone segments. We also utilized computer-aided manufacturing to produce cutting guides, positioning guides, and models to increase precision and improve the final positioning of the cranium. Computer-aided design and manufacturing technology has enabled a shift of the planning burden of complicated craniofacial reconstructions from the intraoperative to the preoperative phase. With a completed plan and premade guides and models, it is possible to minimize the risks, improve efficiency, and obtain a precise, aesthetic result.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".