Anemia and iron metabolism disorders after single anastomosis sleeve ileal (SASI) bypass. Is it a real problem?
Bibliographic record
Abstract
Abstract Purpose SASI (single anastomosis sleeve ileal) bypass can lead to nutritional deficiencies, including disorders of iron metabolism and anemia. This study aims to evaluate the effect of SASI bypass on weight loss, anemia, and iron deficiency in patients with obesity during the follow-up period. Methods This study is a retrospective analysis of prospectively collected data from patients who underwent SASI bypass at our hospital between January 2020 and February 2022. Results The mean age of the patients was 42 years (range 22–58). The average duration of the follow-up period was 26 months. The mean percentage of excess weight loss (%EWL) was 90.1%, and total weight loss (%TWL) was 30.5%. During the postoperative observation period, anemia was identified in ten patients (25%), comprising 70% with normocytic anemia, 10% with microcytic anemia, and two macrocytic anemia cases (20%). Iron deficiency was observed in two patients (5%). Conclusion SASI bypass is an effective bariatric procedure in weight loss outcomes. However, in our follow-up period, there may be an elevated risk of anemia and disruptions in iron metabolism associated with this procedure. This indicates the need to monitor iron homeostasis parameters periodically and consider permanent supplementation in patients after SASI bypass, especially at prolonged postoperative intervals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".