Iron supplementation following bariatric surgery: A systematic review of current strategies
Bibliographic record
Abstract
Iron deficiency (ID) and iron deficiency anemia (IDA) are common following bariatric surgery; however, there are limited standardized treatment recommendations for their management. The purpose of this study was to review the current strategies for iron supplementation following bariatric surgery and assess their relative efficacy in managing ID and IDA. MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials were searched to January 2021. Primary outcomes of interest were prevention or improvement in ID or IDA with iron supplementation. Forty-nine studies with 12,880 patients were included. Most patients underwent Roux-en-Y gastric bypass (61.9%). Iron supplementation was most commonly administered orally for prevention of ID/IDA and was effective in 52% of studies. Both IV and oral iron were given for treatment of ID/IDA. Fifty percent (3/6) of the oral and 100% (3/3) of the IV supplementation strategies were effective at treating ID. Iron supplementation strategies employed following bariatric surgery are highly variable, and many do not provide sufficient iron to prevent the development of ID and IDA, potentially due to poor patient adherence. Further high-quality prospective trials, particularly comparing intravenous and oral iron, are warranted in order to determine the ideal dosage, route, and duration of iron supplementation.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".