Penetrating Heart Injuries and Common Difficulties Encountered During Emergency Surgery
Bibliographic record
Abstract
Background: Penetrating heart injuries are seldom but are highly lethal traumas. In-house cardiac surgery teams and adequate technical equipment are generally not found outside of major health institutions in Turkey. We evaluate the diagnosis and treatment of penetrating heart injuries, the difficulties encountered during surgical treatment of such conditions as well as problems faced by hospitals with limited cardiac surgery manpower and lacking adequate equipment to deal with such incidents. Methods: The diagnosis of ‘penetrating heart injury’ between 1 January 2008 and 31 December 2009 was scanned through hospital data processing system. Eleven patients presenting to Kahramanmaras State Hospital were retrospectively evaluated. Results: Three (26%) of the patients presented with signs of hypovolemia, four (37%) with progressive shock and four (37%) with shallow respiration in addition to progressive shock. The etiology of the injuries were edged and sharp objects in 9 (82%) cases, puncture from a fractured sternum and/or ribs in 2 (18%) case. The total mortality was 63% in our case series. Conclusion: Cardiac injuries are the types of trauma that require rapid surgical intervention. However, combination of a lack of specialized surgical teams and/or the time for rapid intervention at the initial health care facility reduces the possibility of surviving patient. doi:10.4021/jcs86w
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".