Patient and Hospital Factors Associated With Hospital Course for Patients With Mild Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: To characterize variation in the treatment of patients with mild traumatic brain injury (mTBI) who were reasonable candidates for hospitalization, we investigated patient-level associations with inpatient admission and hospital-level associations with length of stay (LOS). We further investigated whether patients treated at high-LOS hospitals were more likely to experience home discharge. SETTING: Patients were retrospectively identified from the ACS TQIP dataset. PARTICIPANTS: A total of 122 406 patients with mTBI were included. DESIGN: We performed hierarchical logistic regression to investigate associations of patient-level variables with inpatient admission. Among hospitalized patients, a hierarchical linear regression was constructed for associations with LOS, including hospitals as a random effects term. Based on random effects coefficients, hospitals were classified as high-LOS outliers or non-outliers. MAIN MEASURES: Univariable comparisons on facility characteristics were performed. Patients were propensity score matched across hospital outlier status, and a multivariable logistic regression for associations with discharge to home was performed. RESULTS: The median age was 63 years (interquartile range [IQR], 42-77 years), and 111 306 (91%) patients experienced inpatient admission. Uninsured status was associated with lower odds of inpatient admission (odds ratio [OR], 0.71; 95% confidence interval [CI], 0.65-0.76; P < .001). After excluding very low-volume hospitals, 80 258 admitted patients were treated across 469 hospitals, and 98 were designated as high-LOS outliers. These were more likely to be Level 1 trauma centers (76% vs. 26%; P < .001). After matching, patients treated at high-LOS outlier hospitals were less likely to experience home discharge (OR, 0.89; 95% CI, 0.85-0.93; P < .001). This effect was amplified for patients identifying as non-White, non-Black, non-Hispanic other races ( P = .003). CONCLUSIONS: Inpatient admission after mTBI varies by insurance status, with uninsured patients less likely to be admitted. There is significant interhospital variation in LOS, with Level 1 trauma centers more likely to be high-LOS outliers. Despite their longer LOS, patients treated at outlier hospitals experienced lower odds of home discharge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".