The effect of diabetes on burn patients: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Hyperglycemia during the acute phase after burn is associated with increased morbidity and mortality. There is little knowledge regarding the effect of pre-existing hyperglycemia in the form of diabetes on the outcomes after severe burns. The objective is to determine the impact of diabetes on clinical outcomes after burns. METHODS: Single-center cohort study where adult diabetic (n = 76) and non-diabetic (n = 1186) burn patients admitted between 2006 and 2016 were included. Diabetic patients were stratified into those with well-controlled diabetes (n = 24) and poorly controlled diabetes (n = 33) using a HbA1c of 7% as a cutoff; additionally, diabetics were divided into well-controlled glycemia (n = 47) and poorly controlled glycemia (n = 22) based on daily blood glucose measurements during hospitalization. RESULTS: On univariate analysis, diabetics had a significantly increased median length of stay per percent total body surface area burn (2.1 vs. 1.6 days; p = 0.0026) and a greater number of overall morbidity (1.39 ± 1.63 vs. 0.8 ± 1.24; p = 0.001). After adjustment for patient characteristics, diabetics were associated with significantly increased total morbidity (RR 1.5; 95% CI 1.1-1.9). At discharge, almost two thirds of diabetics needed an escalation of anti-diabetic medication and a quarter had newly developed insulin dependency. There were no differences in morbidity or mortality in the diabetic subgroups. CONCLUSIONS: Diabetics had a longer hospitalization and increased morbidity, regardless of the quality of their anti-diabetic therapy prior to injury. Additionally, diabetes in burn patients is associated with an increased risk of total morbidity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".