An allometric model to estimate fluid requirements in children following burn injury
Bibliographic record
Abstract
OBJECTIVES: To evaluate the ability of an allometric 3/4 Power Model combined with the Galveston Formula (Galveston-3/4 PM Formula) to predict fluid resuscitation requirements in children suffering burn injuries in comparison with the frequently used Parkland Formula and Galveston Formula using the Du Bois formula for surface area estimation (Galveston-DB Formula). AIM: To demonstrate that the Galveston-3/4 PM Formula is clinically equivalent to the Galveston-DB Formula for the estimation of fluid requirements in pediatric burn injury cases. BACKGROUND: Fluid resuscitation requirements differ in children suffering burn injuries when compared to adults. The Parkland Formula works well for normal weight adults but underestimates fluid requirements when indiscriminately applied to pediatric burn patients. The Galveston-DB Formula accounts for the change in body composition with age by using a body surface area (BSA) model but requires the measurement of height. The allometric model, using an exponent of 3/4, accounts for the dependence of a physiological variable on body mass without requiring height measurement and can be applied to estimate fluid requirements after burn injury in children. METHODS: Comparisons were performed between the hourly calculated fluid requirements for the first 8 h following 20%, 40%, and 60% BSA burns using the Parkland Formula, the Galveston-DB Formula and Galveston-3/4 PM Formula for children 2-23 kg. RESULTS: In children less than 23 kg, the fluid requirements predicted by the Galveston-3/4 PM Formula are well correlated with those predicted by the Galveston-DB Formula (R2 = 0.997, P < 0.0001) and are much better than of the predictions made with the Parkland Formula, especially for children <10 kg. CONCLUSIONS: For the purposes of clinical estimation of fluid requirements, the Galveston-3/4 PM Formula is indistinguishable from the Galveston-DB Formula in children 23 kg or less.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".