Chest Wall Resection and Reconstruction Following Cancer
Bibliographic record
Abstract
The chest wall represents a complex musculoskeletal structure that provides protection to intrathoracic organs, mechanical support for respiration, and mobility for the upper limbs. Neoplastic diseases of the chest wall encompass a heterogeneous group of benign and malignant lesions, which may be classified as primary-originating from bone, cartilage, muscle, or soft tissue-or secondary, resulting from direct invasion or metastatic spread, most commonly from breast or lung carcinomas. Approximately half of all chest wall tumors are malignant, and their management remains a significant diagnostic and therapeutic challenge. Surgical resection continues to represent the mainstay of curative treatment, with complete en bloc excision and adequate oncologic margins being critical to minimize local recurrence. Advances in reconstructive techniques, including the use of prosthetic materials, biological meshes, and myocutaneous flaps, have markedly improved postoperative stability, respiratory function, and aesthetic outcomes. Optimal management requires a multidisciplinary approach involving thoracic and plastic surgeons, oncologists, and radiotherapists to ensure individualized and comprehensive care. This review summarizes current evidence on the classification, diagnostic evaluation, surgical strategies, and reconstructive options for chest wall tumors, emphasizing recent innovations that have contributed to improved long-term survival and quality of life in affected patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".