Optimising surgical efficiency by designing lightweight skull-PSI assemblies and curved fixture plates for cranial reconstruction using FEA
Bibliographic record
Abstract
Cranial reconstruction using implants is critical for protecting intracranial structures and restoring cerebral hemodynamics in cases of cranial defects caused by accidents, diseases or cancer. Patient-specific implants (PSIs) made from materials such as polyether-ether-ketone (PEEK), are required to be lightweight, high in strength and capable of mimicking the natural bone structure. Effective fastening mechanisms using the required number of fixture plates are essential for seamless integration between the PSI and the cavity of a defected skull for successful cranial reconstruction. This study explores the optimal number and shape of fixture plates required to join a Skull-PSI assembly, such that the overall weight of the PSI remains minimal, and to ensure that these assemblies do not fail when subjected to heavy external loads of 950 N. PEEK material was used for PSI, natural bone for the defected skull and Titanium Alloy (Ti-6Al-4 V) for the fixture plates. Conventional straight shaped fixture plates often require manual bending for correct fitment on the Skull-PSI curved surface, which increases a surgeon's time and effort. Curved shaped fixture plates were designed, to save on this time and effort and enhance the contact surface area with the Skull-PSI surface. Four, three and two numbered, straight and curved shaped fixture plates were investigated using Finite Element Analysis (FEA) techniques. Three numbered, curved shaped fixture plates were found to be optimal, to generate a 7-gram lightweight PSI that could successfully sustain external loads up-to 950 N without failure. Ultimately, these design improvements would benefit both patient and surgeon in aspects of surgery time and patient comfort.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".