Intraoperative Management of Liquids in Liposuction Based on the Variability of the Inferior Vena Cava
Bibliographic record
Abstract
Background: Liposculpture is one of the most common surgical procedures performed, and its success depends on several variables. The most crucial factor is fluid management, which determines faster recovery, shorter hospitalizations, optimized hemodynamic parameters, and adequate urinary output. There is a common misconception regarding the established protocol in clinical practice, resulting in a tendency for patients to become hypovolemic during the recovery period. Methods: A prospective and longitudinal observational study was conducted. We created a database to register different variables from patients who underwent liposuction between August 2022 and December 2024, including 96 patients aged 19-63 years after determining the established inclusion and exclusion criteria. We executed measurements of the IVC as a marker for the hemodynamic status. Results: Echographic diameter of the IVC and its collapsibility and distensibility index are highly sensitive markers to evaluate the response to nonrestrictive fluid management. We found an absence of hypovolemia or hypervolemia, controlled intravascular volume redistribution, and low hemodynamic variability. Conclusions: This study suggested the use of nonrestrictive fluid management and the use of standardized guidelines with ultrasound measurement of the IVC.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".