Effect of bariatric surgery on cardiac function in obese patients
Bibliographic record
Abstract
Objectives: To analyze the clinical and echocardiographic changes in individuals with morbid obesity who underwent bariatric surgery. Methods: In total, 59 obese patients with body mass index >35 kg/m2 were prospectively enrolled. We assessed baseline pre-operative and a 6-month post-operative lipid profile, hemoglobin A1c, echocardiography, lifetime, and a 10-year risks of atherosclerotic disease for all patients. Results: The mean patients’ age was 37±12 years, with 40 (67.8%) women. We found that the pre-operative total cholesterol (4.2±1.1 vs. 4.4±1.1, p=0.014) and triglyceride levels (1.4±0.7 vs. 1.8±0.8, p<0.0001) were significantly lower than post-operative levels, while post-operative high-density lipoprotein levels were significantly higher (1.5±0.5 vs. 1.2±0.3, p<0.0001). The calculated 10-year risk of atherosclerotic cardiovascular disease was significantly lower post-operatively (1.1±1.6% vs. 1.6±1.8%, p<0.0001). Echocardiography follow-up revealed that diastolic dysfunction was more prevalent pre-operatively than that post-operatively (41% vs. 10%, p<0.0001). Post-operative left ventricular (LV) mass was significantly lesser than the pre-operative mass (168±252 g vs. 187±255 g, p=0.019), whereas the post-operative LV diastolic (46.5±7 mm vs. 38.5±18 mm, p=0.002) and systolic dimensions (31±5 mm vs. 25±11 mm, p=0.001) were significantly smaller. Conclusion: Bariatric surgery resulted in a significant amelioration in lipid profile, reduction in LV mass, and LV cavity dimensions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".