Nonskeletal injuries related to cardiopulmonary resuscitation: An autopsy study
Bibliographic record
Abstract
The current standard technique for cardiopulmonary resuscitation (CPR), initially described in the early 1960s, has quickly become the expected response for all persons found without a pulse or respiration. Despite the potentially lifesaving properties of external cardiac massage, the mainstay of resuscitation, it consists of repeated blunt force trauma to the chest, which can lead to extensive traumatic skeletal and nonskeletal injuries. Numerous autopsy-based studies have documented the incidence and patterns of rib and sternal fractures associated with attempted CPR, but there is relatively little data on the incidence and severity of nonskeletal CPR-related injuries. We reviewed reports from 1878 autopsies performed between September 2017 and December 2019 (inclusive), for documentation of CPR-related injuries. Among these cases, there were 93 cases with resuscitation-related nonskeletal injuries. The most common type of injury identified were visceral contusions, documented in 57.0% of cases. These contusions predominantly involved the heart, lungs, neck soft tissue, and surrounding structures. Resuscitation-related lacerations were seen in 17.2% of the cases, most predominantly involving the pericardium, heart, and liver. Statistical analysis of the data demonstrated that lacerations were more likely to be seen in females and with associated sternal fractures. Additionally, hemothoraces were present in 34.4% of cases and hemopericardium was seen in 8.6% of cases. This study provides additional documentation of the range, severity, and incidence of various types of resuscitation-related visceral injuries to better assist autopsy pathologists in distinguishing these injuries from other antecedent traumatic injuries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".