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Record W4415246969 · doi:10.1177/00031348251388956

Impact of Psychiatric Illness on Clinical Outcomes of Patients With Multiple Rib Fractures: Insights From a National Trauma Database

2025· article· en· W4415246969 on OpenAlex
Danielle A Rowe, Krishna Ruthra, Shangar Muhunthan, Vladimir Rubinshteyn, Loren J. Harris, Nisha Lakhi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe American Surgeon · 2025
Typearticle
Languageen
FieldMedicine
TopicTrauma Management and Diagnosis
Canadian institutionsUniversity of TorontoUniversity of Alberta
Fundersnot available
KeywordsComorbidityARDSIntensive care unitUnivariate analysisPneumoniaLogistic regressionRetrospective cohort studyPulmonary embolismDistress

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.357
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it