How Well Does The Parkland Formula Estimate Actual Fluid Resuscitation Volumes?
Bibliographic record
Abstract
We had anecdotally observed that fluid resuscitation volumes often exceed those estimated by the Parkland Formula in adults with isolated cutaneous burns. The purpose of this study was to compare estimated and actual fluid resuscitation volumes using the Parkland Formula. We performed a retrospective study of fluid resuscitation in patients with burns > or = 15% TBSA. Patients with inhalation injury, high voltage electrical injury, delayed resuscitation, or associated trauma were excluded. We studied 31 patients (mean age 51 +/- 20 years, mean TBSA burn 27 +/- 10%). The 24 hour resuscitation volume of 13 354 +/- 7386 ml (6.7 +/- 2.8 ml/kg/%TBSA) was significantly greater than predicted (P = 0.001) and exceeded estimated volume in 84% of the patients. The mean urine output in the first 24 hrs was 1.2 +/- 0.6 ml/kg/hr. After the first 8 hours of resuscitation, the infusion rate decreased by 34% in 16 patients (DCR group), while in 15 patients the rate increased by 47% (INCR group). Both the DCR and INCR groups received significantly more fluid than predicted, (5.6 +/- 2.1 ml/kg/%TBSA and 7.7 +/- 3.1 ml/kg/%TBSA respectively). The INCR patients had significantly larger full thickness burns (14 +/- 11% vs 3 +/- 6%, P < 0.001). Our findings reveal that despite its effectiveness, the Parkland Formula underestimated the volume requirements in most adults with isolated cutaneous burns, and especially in those with large full thickness burns.
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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 | 0.005 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".