601 Comparison of Healthcare Resource Utilization in Burns, Frostbite and Necrotizing Fasciitis: A Propensity Score-based Analysis
Bibliographic record
Abstract
Abstract Introduction Patients with burns, frostbite, or necrotizing fasciitis (NF) are recognized to have high rates of healthcare resource utilization (HRU). However, a comparison of HRU between these three groups at a single centre is not well known in the literature. Historically, the HRU of these three groups has not been differentiated with respect to hospital administrative planning. The goal of this study is to compare HRU between these three groups. Methods This is a retrospective review of adults acutely admitted for injuries due to burn (n=589), frostbite (n=144) or NF (n=421) at a single Canadian level 1 centre. Two cohorts (burns versus frostbite, burns vs NF) will be created with a ratio of approximately 1:1, propensity-score nearest neighbor matched on age, sex, and geographic location. Descriptive analysis will examine patient characteristics. HRU analysis examines length of hospital stay, number of operations, outcomes (% with autograft, free tissue transfer, in-hospital mortality). Results NF patients had significantly mean longer length of stay (32.7d) versus frostbite (24.5d) and burns (18.5d) (p< 0.001). Similarly, a significantly higher proportion of NF patients stayed in ICU (47.2%) versus burns (30.2%) (p< 0.001) as well as free tissue transfer (6.1% vs 3.5% vs 2.4%). Frostbite patients had the highest proportion of amputation (41.7% versus NF with 14.7% and burns with 3.2%) Conclusions Generally, NF patients required the highest amount of HRU followed by frostbite and burn patients. Applicability of Research to Practice The implications of this study are that burn, frostbite, and NF patients have different rates of HRU, which can directly impact hospital administration resource allocation. Funding for the Study N/A
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".