Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE OF REVIEW: Fluid (volume) therapy is an integral component in the management of critically ill patients and fluid management may influence outcome. There is much controversy, however, about the type, timing and amount of fluid therapy. Here, we discuss the evidence available to guide such choices. RECENT FINDINGS: Fluid therapy is widely endorsed for resuscitation of critically ill patients across a range of conditions. Yet, the approach to fluid therapy is subject to substantial variation in clinical practice. Emerging data show that the choice, timing and amount of fluid therapy may affect clinical outcomes. Synthetic colloids may increase the risk of acute kidney injury. Albumin may benefit hypoalbuminemic patients with sepsis and acute lung injury but may worsen outcome in traumatic brain injury. Early administration of fluid therapy in sepsis may improve survival but may be unnecessary in patients with penetrating trauma. Later fluid therapy in acute lung injury patients will increase the duration of ventilator dependence without achieving better survival. A positive cumulative balance likely contributes to increased morbidity and mortality after major surgery. SUMMARY: Emerging evidence shows that choice, timing and amount of fluid therapy affect outcome. Future studies need to focus on these aspects of fluid therapy by means of larger, more rigorous and blinded controlled trials.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it