Bibliographic record
Abstract
PURPOSE OF REVIEW: We discuss the formulation of a prescription for intravenous (i.v.) fluid therapy (a 'volume prescription') for critically ill patients: pros/cons of different fluid types; accurate dosing; and qualitative and quantitative toxicities. Updated physiologic concepts are invoked and results of recent major clinical trials on i.v. fluid therapy in the acutely ill are interpreted. RECENT FINDINGS: Context is vital and any fluid can be harmful if dosed incorrectly. When contrasting 'crystalloid versus colloid', differences in efficacy are modest, but differences in safety are significant. Differences in chloride load and strong ion difference appear to be clinically important. Quantitative toxicity is mitigated when dosing is based on dynamic parameters that predict volume responsiveness. Qualitative toxicity for colloids (even with newer hydroxyethyl starch 130/0.4 solutions) and isotonic saline remain a concern. SUMMARY: Similar to any drug used in acutely ill patients, clinicians ordering a volume prescription must recognize that context is crucial. Physiologically balanced crystalloids may be the 'default' fluid for acutely ill patients, and the role for colloids is unclear. Optimal dosing involves assessment of volume responsiveness.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".