Bibliographic record
Abstract
Recognition of fluid creep has driven a large amount of the scientific investigation in the area of acute fluid resuscitation for burn patients. The role of colloids in ameliorating fluid creep is controversial, despite the fact that a fluid-sparing effect of colloids has been recognized for some time. All but one of the available prospective studies using colloids are more than a decade old, and a modern randomized controlled trial (RCT) comparing crystalloids to colloids is long overdue. While urinary output continues to be the main endpoint for fluid titration, there has been a moderate amount of interest in the use of transpulmonary thermodilution to guide fluid resuscitation. The available studies have found that transpulmonary thermodilution has had an inconsistent effect on limiting fluid resuscitation volumes and improving clinical outcomes. Computerized Decision Support Systems show great promise in optimizing fluid titration and reducing fluid resuscitation volumes, and an RCT comparing Computerized Decision Support Systems with conventional titration approaches will be the important next step. Use of high-dose vitamin C (ascorbic acid) has become a popular approach to limit fluid resuscitation volumes and edema formation, but it has been investigated in only two clinical studies: one a pseudo-randomized prospective study and the other a retrospective study. Improvements in clinical outcome have not been convincingly demonstrated, and concerns persist surrounding the possibility of induction of an osmotic diuresis, leading to intravascular volume depletion. An RCT is urgently required to evaluate high-dose vitamin C as an adjunct to crystalloid resuscitation compared with the use of crystalloids alone.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.054 | 0.017 |
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".