Effect of obesity on patterns and mechanisms of injury: Systematic review and meta analysis
Bibliographic record
Abstract
BACKGROUND: Trauma is one of the major causes of morbidity and mortality globally, especially in younger populations. With an increase in obesity globally, it is important to assess the potential differences in injury pattern and management of obese patients presenting with trauma compared to lean patients to try and improve patient care. This study aims to review the available literature comparing injury patterns between obese and non-obese patients. METHODS: A systematic review of articles which assessed the association between obesity and different patterns of trauma and injury were assessed. Study quality was assessed using the Newcastle-Ottawa Scale. Data for injury patterns, type of trauma and patient course and outcome including length of ITU stay, length of intubation, length of hospital stay and mortality were collected. Summary data was assessed with random-effects meta-analysis. RESULTS: 21 studies with a total of 2,977,758 patients were identified. The majority of included patients had suffered blunt trauma. Lean patients were found to have a slightly increased risk of blunt trauma as a mechanism of injury (RR 1.02 p=<0.001), with a greater risk of head injury (lean patients RR 1.38 p=<0.001), reduced likelihood of extremity (lean RR 0.79 p=<0.001), and thoracic injury (lean RR 0.83 p = 0.002). There was no difference in risk of abdominal injury (p = 0.39). DISCUSSION: Obese trauma patients present with different injury patterns compared to lean patients. This may have significant impact on resource requirements and patient outcomes and must be considered when planning or allocating resources for trauma care. Whether differing injury patterns relate to body habitus, lifestyle factors, or other confounders is unclear, but likely represents a combination of thereof.
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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.041 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".