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Record W4416711465 · doi:10.1097/gox.0000000000007256

Intraoperative Management of Liquids in Liposuction Based on the Variability of the Inferior Vena Cava

2025· article· en· W4416711465 on OpenAlexaff
Giovanni Mera-Cruz, Natalia Murillo-Romero, Hernando Carrascal-Carrasquilla, Octavio de Jesus Carrascal-Navarro, Carlos Lacouture-Armenta, Silvia Daniela Pabón-Rojas, Lauren Escandón-Salazar, Isabella Mera-Herrera, Juan José Mera-Herrera

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2025
Typearticle
Languageen
FieldMedicine
TopicBody Contouring and Surgery
Canadian institutionsColumbia College
Fundersnot available
KeywordsLiposuctionInferior vena cavaUltrasoundVena cavaComplication

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.270
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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