Bibliographic record
Abstract
PURPOSE OF REVIEW: It has recently become evident that administration of intravenous fluids following initial resuscitation has a greater probability of producing tissue edema and hypoxemia than of increasing oxygen delivery. Therefore, it is essential to have a rational approach to assess the adequacy of volume resuscitation. Here we review passive leg raising (PLR) and respiratory variation in hemodynamics to assess fluid responsiveness. RECENT FINDINGS: The use of ultrasound enhances the clinician's ability to detect and predict fluid responsiveness, whereas enthusiasm for this modality must be tempered by recent evidence that it is only reliable in apneic patients. SUMMARY: The best predictor of fluid response for hypotensive patients not on vasopressors is a properly conducted passive leg raise maneuver. For more severely ill patients who are apneic, mechanically ventilated and on vasopressors, point of care echocardiography is the best choice. Increases in vena caval diameter induced by controlled positive pressure breaths are insensitive to arrhythmias and can be performed with relatively brief training. Most challenging are patients who are awake and on vasopressors; we suggest that the best method to discriminate fluid responders is PLR measuring changes in cardiac output.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| 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) | 0.004 | 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".