Finite element analysis of distraction osteogenesis with a new extramedullary internal distractor
Bibliographic record
Abstract
Distraction osteogenesis (DO) is a bone regenerative maneuver, which is conventionally done with external fixators and, more recently, with telescopic intramedullary nails. Despite the proven effectiveness, external approaches are intrusive to the patient's life while intramedullary nailing damages the growth plates, making them unsuitable for pediatric patients. An internal DO plate fixator (IDOPF) was developed for pediatric patients to address these limitations. The objective of this study was to test the hypothesis that the IDOPF can withstand a partial weight bearing scenario and create a favorable mechanical microenvironment at the osteotomy gap for bone regeneration as the device elongates. A finite element model of a surrogated long bone diaphysis osteotomy fixation by means of the IDOPF was created and subjected to axial compression, bending and torsion. As the osteotomy gap increased from 2 mm to 20 mm, under compression, The average axial interfragmentary strains decreased from 2.33% to 0.35%. Stress increased from 179 MPa to 281 MPa at the contact interfaces of the telescopic compartments, which exceeded the endurance limit of stainless steel (270 MPa) but was below its yield limit (415 MPa). These results demonstrate, that the IDOPF can withstand a partial load bearing scenario and provide a stable biomechanical environment conductive to bone healing. However, high contact stresses at the telescopic interfaces of the device are likely to cause wear, as is frequently reported in telescopic fixators. This study is a step towards refining the IDOPF design for clinical use.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".