A narrative review of traumatic mediastinal injuries and their management: the thoracic surgeon perspective
Bibliographic record
Abstract
OBJECTIVE: Mediastinal injuries are uncommon, rarely encountered and depending on the institution, can be managed by various sub-specialties. The purpose of this narrative review is to present an overview of traumatic mediastinal injuries, their presentation, and management options from the perspective of a thoracic surgeon. BACKGROUND: Although infrequent, traumatic mediastinal injuries can pose significant morbidity and mortality. The infrequency of these injuries limits operative exposure for thoracic surgeons and trainees. A concise overview of common presentations and management options is warranted to further solidify important concepts. METHODS: A search of the literature was conducted using MEDLINE, PubMed, and Embase for relevant articles pertaining to anatomic injuries of the mediastinum. The presentation of mediastinal injuries along with indications for non-operative versus operative management in cardiac injuries, thoracic esophageal injuries, tracheobronchial injuries, and injuries to the lungs and pleura was conducted and literature summarized. CONCLUSIONS: In providing this review it is hopeful to enhance knowledge and comfort in recognition and management of these uncommon yet potentially lethal injuries. Early involvement of thoracic surgery is recommended to ensure effective and efficient treatment.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".