Blunt cardiac trauma: a narrative review
Bibliographic record
Abstract
OBJECTIVE: This review aims to summarize the literature regarding blunt cardiac trauma (BCT), focusing on the mechanism of injury, presentations, diagnostic, and treatment approaches to provide an up-to-date perspective and to identify the areas requiring further investigation. BACKGROUND: BCT is a relatively rare, but likely underdiagnosed, consequence of blunt chest trauma. BCT occurs in less than 10% of all trauma admissions although it is responsible for approximately 25% of traumatic deaths. BCT often occurs due to forceful impact, rapid deceleration, or crush injuries and the most common causes include motor vehicle accidents, pedestrians struck by motor vehicles, and falls from a significant height. While BCT is certainly an important condition with significant clinical implications, the current literature is limited comprised of mainly case reports. BCTs are often complex cases with various presentations, comorbid injuries, diagnostic modalities, and treatments. METHODS: PubMed was searched for retrospective, prospective, randomized control trials, case report studies, and previous related reviews investigating BCT published from January 1, 1985 to February 1, 2021. Exclusion criteria included if the full length manuscript was not available and if insufficient data was provided regarding the patient outcomes or the case presented in case reports. CONCLUSIONS: A wide variety of injuries, mechanisms, presentations, and treatments are possible with BCT. Commonalities in initial investigations include a thorough history and physical exam where appropriate, lab work to evaluate patient status, and imaging to diagnose structural injuries. Treatment often begins with stabilization of the patient and rapid initiation of interventions aimed at underlying structural or rhythm abnormalities. As the current literature is limited, further study into BCT is necessary. As BCT is relatively rare and occurs sporadically at individual centers, widespread data collection from BCT patients regarding their treatment and outcomes is essential in order to collect the required data for retrospective or prospective studies. The widespread establishment of multicenter or national databases may be a solution to address the limited data in this field allowing identification of optimal treatments for BCT patients, addressing the limitations in the field of BCT, and allow the continued improvement in the outcomes of patients who experience BCT.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".