The Use of Transesophageal Echocardiography for Preload Assessment in Critically Ill Patients
Bibliographic record
Abstract
UNLABELLED: IV volume is often administered to patients in an intensive care unit (ICU) to improve cardiovascular function. We investigated the relationship between stroke volume (SV) and left ventricular (LV) size by using transesophageal echocardiography (TEE) in a population of 20 ICU patients and 21 postoperative cardiac surgical patients. We also examined whether LV end diastolic area (EDA), by TEE, could identify patients who increased SV by 20% or more (responders) after 500 mL of pentastarch administration. There was only a modest relationship (r = 0.60) between the EDA and the SV in all patients. No relationship could be found between the pulmonary capillary wedge pressure (PCWP) and the EDA in all patients. Both responder and nonresponder PCWP increased significantly after volume administration. Only responder EDA increased significantly after volume administration. Responders had significantly lower EDA (15.3 +/- 5.4 cm(2)) and PCWP (12.2 +/- 2.2 mm Hg) when compared with nonresponders (20.2 +/- 4.8 cm(2)) and 15.9 +/- 3.1 mm Hg, respectively). Few ICU patients and only those with a small EDA responded to volume administration. It was not possible to identify an overall optimal LV EDA below which most patients demonstrate volume-recruitable increases in SV. IMPLICATIONS: In a ventilated intensive care unit and cardiac surgical population, transesophageal echocardiography and pulmonary artery catheter are sensitive in detecting changes in preload after volume administration. Few patients demonstrate volume-recruitable increases in stroke volume when compared to cardiac surgical patients. It is not possible to establish an overall end diastolic threshold below which a large proportion of ventilated patients respond to volume administration.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".