Cardiovascular and liver disease among pre-hospital trauma deaths: A review of autopsy findings
Bibliographic record
Abstract
Introduction Pre-existing disease is a common contributor to mortality and morbidity after injury and resuscitation of injured patients are often altered in hospital based on comorbidities. However, this is uncommon in the pre-hospital phase of care where patients are managed according to clinical practice guidelines. This study aimed to quantify the prevalence of cardiovascular disease (CVD) and liver disease among trauma patients attended by pre-hospital clinicians but who died prior to reaching hospital and assess associations with age. Methods This was a retrospective review of pre-hospital trauma deaths in the state of Victoria, Australia between 01 Jan 2008 and 31 Dec 2014. The inclusion criteria were (a) patients attended by pre-hospital clinicians, (b) deceased before arrival to hospital, (c) evidence of recent trauma and (d) underwent a full autopsy. Cardiovascular and liver disease status were extracted from autopsy reports. Results There were 1043 patients included in this study. Most patients were male (77.1%). Intentional self-harm was significantly more common in patients aged ≥65 years (17.4%). CVD was prevalent in 495 (47.5%; 95%CI: 44.4–50.5) cases with myocardial fibrosis the most common abnormality detected. All sub-groups of CVD demonstrated a significant association with increasing age, except right ventricular hypertrophy. Liver disease was present in 235 (22.5%; 95%CI: 20.1-25.2) patients and most common among patients aged 35–64 years. Discussion CVD was prevalent in almost half of all injured patients included in this study while liver disease was present in about a fifth. The prevalence of CVD was associated with increasing age, while liver disease was more common among middle-aged patients. This high prevalence in our population indicates that pre-existing cardiovascular and liver disease be considered when tailoring pre-hospital life-saving interventions for injured patients.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".