The Best of Chest Wall Reconstruction: Principles and Clinical Application for Complex Oncologic and Sternal Defects
Bibliographic record
Abstract
LEARNING OBJECTIVES: After studying this article, the participant should be able to: 1. Appraise and evaluate risk factors for respiratory compromise following oncologic resection. 2. Outline and apply an algorithmic approach to reconstruction of the chest wall based on defect composition, size, and characteristics of surrounding tissue. 3. Recognize and evaluate indications for and types of skeletal stabilization of the chest wall. 4. Critically consider, compare, and select pedicled and free flaps for chest wall reconstruction that do not impair residual respiratory function or skeletal stability. SUMMARY: Chest wall reconstruction restores respiratory function, provides protection for underlying viscera, and supports the shoulder girdle. Common indications for chest wall reconstruction include neoplasms, trauma, infectious processes, and congenital defects. Loss of chest wall integrity can result in respiratory and cardiac compromise and upper extremity instability. Advances in reconstructive techniques have expanded the resectability of large complex oncologic tumors by safely and reliably restoring chest wall integrity in an immediate fashion with minimal or no secondary deficits. The purpose of this article is to provide the reader with current evidenced-based knowledge to optimize care of patients requiring chest wall reconstruction. This article discusses the evaluation and management of oncologic chest wall defects, reviews controversial considerations in chest wall reconstruction, and provides an algorithm for the reconstruction of complex chest wall defects. Respiratory preservation, semirigid stabilization, and longevity are key when reconstructing chest wall defects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".