Combination of Solid and Porous Nitinol Implants in Surgical Treatment of Extensive Post-Excision Thoracic Defects in Cancer Patients
Bibliographic record
Abstract
Abstract Radical surgical intervention for chest wall tumors is typically accompanied by the lesion of osteochondral structures and the appearance of complex post-resection defects, which result in functional and aesthetic impairment. After extensive resection of the chest wall, it is vitally important that it be simultaneously repaired, including restoring the osteochondral framework and the integrity of the integumentary tissues as well as maintaining the anatomical and physiological volume of the mediastinum and the pleural cavities. Porous and solid Nitinol implants and their successful deployment in surgical treatments have encouraged insights for immediate and delayed osteoplasty in cancer patients. The novel aspect of this work consists in this surgical method of post-excision defect repair is performed using a proprietary approach and customized NiTi-based implants. The results indicate that the suggested surgical approach and tactics using one-step repair are one of the promising techniques even though the case is aggravated with extensive chest wall lesions. The approach can be performed safely and can be recommended as a routine procedure with a high success rate. Combined Nitinol implants seem to be very good reinforcing biomaterials that enabled the reliable repair of thoracic post-excisional defects of various sizes with good functional, clinical, and cosmetic outcomes.
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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 | 0.000 |
| 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 | 0.000 |
| 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".