408 The Effect of Hypoglycemia and Glucose Control on Patient Outcomes after Burn Injury
Bibliographic record
Abstract
Burn injuries are associated with an increase in glucose production and insulin resistance. Hypermetabolism causes further protein catabolism that feeds into gluconeogenesis, thus exacerbating the hyperglycemic state. The purpose of this study is to better understand how hypoglycemia can affect patient outcomes in burn injuries, and what effect glucose control has on these outcomes. This is a retrospective study from patients admitted between 2006 and 2016. Inclusion criteria included adult (≥18 years) patients with ≥ 15% total body surface area (TBSA) burn and at least one glucose measurement. Data included demographics, point-of-care-testing (POCT) glucose levels, laboratory glucose measures, insulin administration, and oral anti-diabetic drugs (metformin). These values were collected for the first thirty days post-injury. Patients were grouped based on their lowest glucose measure and stratified into hypoglycemic and normoglycemic. There were 438 patients included, with 83 patients in the hypoglycemic and 355 patients in the normoglycemic in the group. Overall mean TBSA was 29% ± 15%. There was a significant difference in mean age, injury severity, and TBSA (p<0.05). Median length of stay was significantly increased in the hypoglycemic group: 53 (30-80) vs. 22 (16-37) days (p<0.0001). A significantly greater proportion of patients in the hypoglycemic group did not survive (33% vs. 10%; p<0.0001). Episodes of hypoglycemia in hospital is associated with poor clinical outcomes when adjusted for injury severity. However, maintaining adequate glucose control in the acute care setting is imperative. Greater use of strategies to minimize hypoglycemia is necessary.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".