Iron deficiency in bariatric surgery patients: a single-centre experience over 5 years
Bibliographic record
Abstract
Background: As the prevalence of obesity has increased, so too has the demand for bariatric surgery. This study aimed to determine the incidence of postoperative iron deficiency and anemia and the impact of an increased preoperative ferritin target on postoperative outcomes. Methods: Patients undergoing bariatric surgery in Winnipeg from 2010 to 2014 were included in the analysis. Data capture included age, sex and date of surgery and iron, ferritin and hemoglobin levels before surgery and 12 months postoperatively. Before 2014, there was no protocol for preoperative iron supplementation at our centre; in 2014, a more aggressive preoperative iron supplementation program was introduced to target a minimum preoperative ferritin level of 50 mg/L. Data were analyzed using unpaired t tests, paired t tests and χ2 tests. Results: A total of 399 patients were considered; 288 were included in the analysis. The incidence of iron and ferritin deficiency and anemia at 12 months postoperatively was 14.6%, 9.3% and 15.0%, respectively. In patients who underwent surgery before 2014, the 12-month postoperative levels of iron and ferritin were 12.9 mmol/L and 64.0 mg/L, respectively; patients who underwent surgery in 2014 had levels of 18.3 mmol/L and 124.0 mg/L, respectively (all p = 0.001). The 12-month postoperative hemoglobin levels did not significantly differ between the 2 groups. Conclusion: Bariatric surgery performed with more aggressive preoperative iron supplementation is associated with increased iron and ferritin levels at 1 year postoperatively. As this improves overall clinical outcomes by avoiding iron deficiency and anemia, a minimum preoperative ferritin target should be implemented in metabolic and bariatric surgery programs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".