Bibliographic record
Abstract
Penetrating transmediastinal injury (TMI) is associated with a high mortality rate and presents a challenging diagnostic scenario. Previous dogma mandated surgical exploration or extensive and invasive investigations for all patients sustaining transmediastinal penetrating trauma, regardless of hemodynamic status. Since the late 1990s, the paradigm has changed, with most centers adopting a tiered approach to management based on clinical presentation. Transmediastinal penetrating trauma is a rare injury pattern and can result from gunshot wounds, stab wounds, blast injuries, and other missiles. The most predominant source, however, remains gunshot wounds, accounting for the vast majority of these injuries. A systematic approach in the emergency department to diagnosis and management should be undertaken and patients in extremis or with hemodynamic compromise rapidly identified. The unstable patient forgoes further investigations and the surgeon must use knowledge about the hypothesized trajectory, results of limited imaging, chest tube output, and anticipation of resuscitative maneuvers to select the best operative approach. In patients who are sufficiently stable to undergo CT angiogram (CTA) of the chest, the trajectory of the missile or impalement can often be deduced and this is used to guide further investigation or operation. In those where ambiguity remains, more focused tests such as echocardiography, pericardial window, esophagoscopy or esophagography, and bronchoscopy can be used to assess the mediastinal structures. For the stable patient, management proceeds with cautious and expeditious investigations to determine the extent of underlying organ-specific injuries. Thus, in patients with this injury pattern, determination of the patient's clinical status is critical to determine the appropriate course of management.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".