572 Trace Element Supplementation in Burn Patients: A Quality Improvement Project
Bibliographic record
Abstract
Abstract Introduction Patients with major burns suffer deficiencies in trace elements (TE), which can affect their clinical course. We established a quality improvement (QI) project to supplement TE (multivitamins with TE, vitamin C, and zinc). Herein, we assessed the impact of QI implementation on outcomes. Methods We queried the burn registry for burn patients admitted from 8/1/2019-5/31/2022 who weighed > 40 Kg and stayed >14 days. Demographics; comorbidities; injury, and hospital course information were collected. Post-implementation, TE levels (zinc, copper, and selenium) as well as C reactive protein levels were measured at 2 weeks. Analyses were performed to assess differences between the pre- and post-groups and post-group patients with major burns (≥20%TBSA) and smaller burns (< 20%TBSA). P < 0.05 was considered significant. Results We included 111 patients, 45 in the pre- and 66 in the post-group. Overall, the population was male (79.3%) with a median age of 53 years. Age and sex were not significantly different between the pre- and post-groups. The post-group had higher TBSA (p = 0.005). They were more likely to smoke (p =0.018) and have an alcohol use disorder (p = 0.035). There was no significant difference observed in mortality, complications, or LOS/TBSA (2.1 days [1.2-5.5] vs. 1.7 days [1.2-3.1], p = 0.300) between the groups. Focusing on the post-group, 37 presented with smaller burns and 29 with major burns. Patients with major burns were younger (p =0.003) and healthier than patients with smaller burns. At 14 days, 81.5% and 33.3% of patients with major burns were deficient in zinc and copper, respectively. Zinc (47.6 µg/dL vs.79.2 µg/dL p < 0.001), copper (80.2 µg/dL vs.121.9 µg/dL p < 0.001), and selenium (98.7 µg/L vs.135.5 µg/L, p = 0.003) levels were significantly lower in patients with major burns. C-reactive protein levels were high in both groups but significantly higher in patients with major burns (p < 0.001). Patients with major burns were more likely to have infectious complications (p = 0.004); however their LOS/TBSA was significantly lower than that of patients with smaller burns (1.21 days vs. 2.43 days, p < 0.001). Zinc levels of patients with major burns significantly increased overtime from 47.6 µg/dL at 14 days post-admission to 73.5 µg/dL at 53 ± 16 days (p < 0.01). Conclusions Our data show that TE supplementation was efficient in bringing zinc levels close to the normal range in patients with burns ≥20%TBSA who stayed up to 50 days in hospital. Controlled for TBSA burned, supplementation of TE significantly decreased LOS of patients with major burns compared to patients with smaller burns. Applicability of Research to Practice Further studies are needed to evaluate the correlation between supplementation of TE and improvement of patient LOS.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".