Liposuction Infiltration: The Quito Formula – a New Approach Based On An Old Concept
Bibliographic record
Abstract
INTRODUCTION: Liposuction is a highly sought after surgical procedure. Despite its popularity, not all of the factors associated with its execution are well understood. No well-established guidelines exist for plastic surgeons regarding the subcutaneous infiltration of fluid and, thus, the procedure is often performed subjectively. OBJECTIVE: To establish the usefulness of the Quito formula (infiltrate volume = weight [kg] × percentage of body surface to be liposuctioned × 2.4 [mL]) for calculating the volume of fluid to be infiltrated subcutaneously during small-volume liposuction performed under epidural anesthesia. METHODS: A prospective study was conducted on a group of 50 patients who were candidates for liposuction on multiple body parts between November 2004 and February 2010. RESULTS: The maximum volume of infiltrate was 5000 mL and the maximum volume of aspirate was 4500 mL, with a 30% total aspirated area. No patient required blood transfusion, and there were no major complications. However, one patient presented with a small local infection, another with a sacral seroma and two patients had postdural puncture headaches. No patient showed clinical signs consistent with overhydration, dehydration, pulmonary embolism, fat embolism or lidocaine intoxication. CONCLUSIONS: When performing small-volume liposuction, subcutaneous infiltration using the Quito formula to calculate the volume of infiltrate proved to be useful, safe and objective.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".