Impact of Psychiatric Illness on Clinical Outcomes of Patients With Multiple Rib Fractures: Insights From a National Trauma Database
Bibliographic record
Abstract
Objective The objective of this study was to compare in-hospital outcomes and mortality in patients with and without a psychiatric comorbidity that presented to a trauma center with isolated blunt chest trauma and multiple traumatic rib fractures. Materials and Methods This is retrospective analysis using the American College of Surgeons Trauma Quality Improvement Program database (2014-2016). Patients ≥18 years with ≥3 traumatic rib fractures were stratified based on the presence or absence of a psychiatric comorbidity. In-hospital complications, length of stay, intensive care unit (ICU) admission, and mortality were assessed. Variables significant ( P < 0.05) on univariate analysis were entered into logistic regression models to determine the independent effect of a psychiatric comorbidity on outcomes. Results Among the 56,558 patients meeting inclusion criteria, 10.6% (n = 6022) had a psychiatric comorbidity. On univariate analysis, patients with a psychiatric comorbidity demonstrated significantly worse in-hospital outcomes, including higher rates of acute respiratory distress syndrome (ARDS) (1.0% vs 0.7%), deep vein thrombosis (DVT) (1.5% vs 1.2%), pulmonary embolism (PE) (0.8% vs 0.5%), pneumonia (4.2% vs 3.1%), urinary tract infection (2.4% vs 1.7%), and decreased mortality (2.2% vs 3.5%). After controlling for comorbidities, substance use, and demographic factors, psychiatric comorbidity was an independent predictor of ARDS (aOR 1.15, P < 0.01), DVT (OR 1.32, P = 0.017), PE (aOR 1.40, P = 0.004), pneumonia (aOR 1.36, P < 0.001), and decreased mortality (aOR 0.71, P < 0.001). Conclusions The presence of a psychiatric comorbidity increases in-hospital complications independent of patient characteristics, comorbidities, and trauma burden in patients presenting with multiple traumatic rib fractures.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".