Financial implications of trauma patients at a Canadian level 1 trauma center: a retrospective cohort study
Bibliographic record
Abstract
Background Trauma is a cause of significant morbidity and mortality globally, and patients with major trauma require specialized settings for multidisciplinary care. We sought to enumerate the variability of costs of caring for patients at a Canadian level 1 trauma center. Methods A retrospective analysis of all adult patients admitted to The Ottawa Hospital trauma service between June 2013 and June 2018 was conducted. Hospital costs and clinical data were collected. Descriptive statistics and multivariable regression analysis using generalized linear model were performed to assess cost variation with patient characteristics. Quintile-based analyses were used to characterize patients in different cost categories. Hospital costs were reported in 2018 Canadian dollars. Results A total of 2381 admissions were identified in the 5-year cohort. The mean age of patients was 50.2 years, the mean Injury Severity Score (ISS) was 18.7, the mean Charlson Comorbidity Index (CCI) score was 0.35, and the median total cost was $10 048.54. ISS and CCI score were associated with higher costs (ISS >15; p<0.0001). The most expensive mechanisms of injury (MOIs) were those involving heavy machinery (median total cost $24 074.38), pedestrians involved in road traffic collisions ($20 965.45), patients in motor vehicle collisions ($17 621.01) and motorcycle collisions ($16 220.89), and acts of self-injury ($13 903.69). Patients who experienced in-hospital adverse events were associated with higher costs (p<0.0001). Our multivariable regression analysis showed variation in costs related to male gender, penetrating/violent MOI, ISS, adverse hospital events, CCI score, urgent admission status, hospital 1-year mortality risk score, and alternate level of care designation (p<0.05). Quintile-based analyses demonstrated clinically significant differences between the highest and lowest cost groups. Discussion Major trauma was associated with high hospital costs. Modifiable and non-modifiable patient factors were shown to correlate with differing total hospital costs. These findings can aid in the development of funding strategies and resource allocation for this complex patient population. Level of evidence Level III evidence for economic and value-based evaluations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".