One anastomosis gastric bypass produces considerable weight reduction and resolution of medical complications with an acceptable rate of nutritional deficiencies
Bibliographic record
Abstract
One anastomosis gastric bypass (OAGB) is a relatively novel bypass surgery variant, increasingly used as a primary surgical procedure. A total of 101 patients (mean age 44.7 ± 10.7 years, mean BMI 47.2 ± 6.6 kg/m2) underwent OAGB between 2014 and 2019 in a single institution. Results obtained 6,12 and 24 months postoperatively were compared with the pre-operative values using "Analyse-it software v5.40.2" and Graphpad Prism v9.3 for figures and tables. Data were tested for normality, described as Mean ± SD, compared using paired sample t-test with 5% p-value for significance and 95% confidence interval (CI). A significant body mass index (BMI), haemoglobin A1c (HbA1c) and low-density lipoprotein (LDL) reduction was recorded throughout follow-up period, with greatest improvement seen 2 years after surgery (47.6 ± 22 kg/m2 vs. 29.4 ± 6.4 kg/m2 ,6.7 ± 1.8% vs. 5.5 ± 0.2% and 3.2 ± 1 vs. 2.05 + 0.7 mmol/ l, p < 0.05). The number of glucose-lowering drugs decreased from 1.6 ± 0.9 to 0.3 ± 0.4 at 24-months, p < 0.001. The rates for zinc, ferritin, folate, B12 and vitamin D deficiency at 24-months were: 8.9%,4%, 5.9%, 0% and 3% respectively. OAGB can effectively downstage obesity disease and improve glucose and lipid homeostasis with a low risk for nutritional deficiencies. This study is one of few to report specifically on the frequency and type of nutritional deficiencies following OAGB surgery.
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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) | 0.001 | 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".