The Impact of Sleeve Gastrectomy on Hyperlipidemia: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Weight loss and reduction in comorbidities can be achieved by longitudinal sleeve gastrectomy (LSG). Existing evidence suggests that LSG resolves or improves hyperlipidemia in morbidly obese patients. The aim of this study was to systematically review the effect of LSG on hyperlipidemia. METHODS: A systematic literature search was conducted from English-language studies published from 2000 to 2012 for the following databases: MEDLINE, EMBASE, CINAHL, PubMed, Clinical evidence, Scopus, Dara, Web of Sciences, TRIP, Health Technology Database, Cochrane library, and PsycINFO. RESULTS: A total of 4,211 articles were identified in the initial search, and 4,185 articles were excluded based on the exclusion criteria. Twenty-six studies met the inclusion criteria for this systematic review, involving 3,591 patients. The mean preoperative body mass index (BMI) was 48 ± 7.0 kg/m(2) (range 37.2-65.3). The mean postoperative BMI was 35 ± 5.9 kg/m(2) (range 26.3-49). The mean percentage of excess weight loss (EWL) was 63.1% (range 37.7-84.5), with a mean followup of 19.1 months (range 6-60). The mean levels of pre and post operative cholesterol were 194.4 ± 12.3 mg/dL (range 178-213) and 181 ± 16.3 mg/dL (range 158-200), respectively. CONCLUSION: Most patients with hyperlipidemia showed improvement or resolution of lipid profiles after LSG. Based on this systematic review, LSG has a significant effect on hyperlipidemia in the form of resolution or improvement in the majority of patients.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".