Hemothorax and needle thoracostomies in prehospital traumatic cardiac arrest: An autopsy series of 172 cases
Bibliographic record
Abstract
Background: Trauma is an important cause of death worldwide, and the majority of deaths from trauma occur in the prehospital setting. The presence of hemothorax contributes to this mortality and is most frequently treated with needle thoracostomies, despite concerns about the effectiveness of this intervention. We present the results of an autopsy series of prehospital traumatic cardiac arrest, describing the frequency of hemothorax in this population and the estimated failure rate of needle thoracostomies. Methods: We used basic demographic data from Emergency Medical Services (EMS) records covering a mixed urban/suburban area in Ontario, Canada, to identify corresponding coroner's reports of cases of prehospital traumatic cardiac arrest. Demographics, injury details, presence and size of hemothorax and prehospital interventions were extracted. Results: Over a 5-year study period, we successfully identified 172 cases of prehospital traumatic cardiac arrest where resuscitation was provided on scene by paramedics. There was a predominantly blunt mechanism of injury (66%) and 96% of patients were in cardiac arrest on EMS arrival. The overall incidence of traumatic hemothorax was 70%. Needle thoracostomies were performed in 40 cases (23%) of traumatic cardiac arrest. Needle thoracostomy failed to decompress a massive hemothorax in 14 out of 33 cases (42%). Conclusions: We identified a high incidence of hemothorax in traumatic cardiac arrest and a high failure rate of needle thoracostomies for decompression of massive hemothorax. Further research is required to explore the feasibility and potential benefits of finger thoracostomy in prehospital traumatic cardiac arrest.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".