Patient and Hospital Characteristics Associated with Admission Among Patients With Minor Isolated Extremity Firearm Injuries: A Propensity-Matched Analysis
Bibliographic record
Abstract
Objective: To quantify the association between insurance and hospital admission following minor isolated extremity firearm injury. Background: The association between insurance and injury admission has not been examined. Methods: This was an observational retrospective cohort study of minor isolated extremity firearm injury captured in the Healthcare Cost and Utilization Project State Inpatient and Emergency Department Databases in 6 states (New York, Arkansas, Wisconsin, Massachusetts, Florida, and Maryland) from 2016 to 2017 among patients aged 16 years or older. The primary exposure was insurance. Admitted patients were propensity score matched to nonadmitted patients on age, extremity Abbreviated Injury Score, and Elixhauser Comorbidity Index with exact matching within hospital to adjust for selection bias. A general estimating equation logistic regression estimated the association between insurance and odds of admission in the matched cohort while controlling for sex, race, injury intent, injury type, hospital profit type, and trauma center designation with observations clustered by propensity score-matched pairs within hospital. Results: A total of 8151 patients presented to hospital with a minor isolated extremity firearm injury between 2016 and 2017 in 6 states. Patients were 88.0% male, 56.6% Black, and 71.7% aged 16 to 36 years old, and 22.1% were admitted. A total of 2090 patients were matched on propensity for admission. Privately insured matched patients had 1.70 higher adjusted odds of admission and 95% confidence interval of 1.30 to 2.22, compared with uninsured after adjusting for patient and hospital characteristics. Conclusions: Insurance was associated with hospital admission for minor isolated extremity firearm injury.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".