Nutritional Deficiencies Before and After Bariatric Surgery in Low- and High-Income Countries: Prevention and Treatment
Bibliographic record
Abstract
Nutritional deficiencies represent a prevalent concern among individuals with obesity, stemming from suboptimal dietary habits, chronic inflammation, and preoperative weight reduction efforts. Bariatric surgical interventions, employing either restrictive, malabsorptive or a combination of the two methods, further compound these deficiencies. Commonly observed nutritional deficits following bariatric surgeries include vitamin B12, vitamin D, thiamine, folate, iron, and protein deficiencies. These deficiencies are further complicated by disparities in healthcare resources and income that distinguish low, medium, and high-income countries. The escalating rates of obesity in low- and medium-income countries are primarily attributed to the increasing availability of cheap, nutritionally depleted, and processed foods, coupled with limited access to healthcare. The provision of bariatric surgical interventions in such regions is hindered by the lack of appropriately trained medical personnel and adequate infrastructure. Additionally, the crucial facets of postoperative care, including diligent follow-up, precise weight loss monitoring, and the administration of appropriate nutritional supplements, often remain lacking. This narrative review provides a comprehensive examination of the prevention and treatment of nutritional deficiencies before and after bariatric surgery in the context of varying healthcare resources and income levels. Bariatric procedures and their global prevalence are discussed, and the prevalence, symptoms, and management strategies of specific nutritional deficiencies are explained. This review also outlines practical strategies for providing more equitable care in low- and medium-income countries.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".