Demographic profiling and cost-analysis of orthopedic care in methamphetamine users at a Canadian level 1 trauma center
Bibliographic record
Abstract
ABSTRACT Background Methamphetamine (meth) is an addictive stimulant that increases risk of trauma and infections. Global meth use continues to rise and the economic impact of meth use on the orthopedic trauma system is poorly understood. This study aims to identify the economic impact meth use exerts on emergent orthopedic care. Methods The meth user cohort was identified using the Manitoba Centre for Health Policy (MCHP) database and compared to a matched control group based on age, sex, income quintile (Q) and orthopedic diagnosis (between 2015-2019). Healthcare spending during the first year following injury was calculated using hospital abstract data and adjusted for inflation. Results A total of 109 meth users were identified, including 49 (45.0%) males and 60 (55.0%) females. Eighty-eight (81.0%) meth users were classified as low-income (Q1-2), while 21 (19.3%) were above low-income (Q3-5). Seventy-two (66.1%) were in the 18-39 age range, while 37 (34.0%) were in the 40-64 age range. Fifty-eight meth users (28 male (48.3%), 30 female (51.7%)) comprised orthopedic diagnoses with unsuppressed data (n>5), including septic arthritis (18), forearm fractures (14), vertebrae fractures (7), tibia fractures (7), spine infections (6) and ankle/hindfoot fractures (6). The one-year healthcare cost-ratios for meth users were: septic arthritis 1.68 (p=0.021), forearm fractures 3.18 (p<0.0001), vertebrae fractures 0.56 (p=0.12), tibia fractures 1.47 (p=0.22), spine infections 0.72 (p=0.40) and ankle/hindfoot fractures 1.30 (p=0.19). The total one-year cost-ratio for meth users was 1.99 (p<0.0001). Conclusion There is significant resource utilization among meth users undergoing emergent orthopedic surgery. Meth users in our study demonstrated higher treatment costs, particularly for septic arthritis and forearm fractures. Healthcare providers and policymakers must prioritize strategies aimed at harm reduction to minimize adverse patient outcomes and healthcare spending.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".