Association of Iron Deficiency Anemia with Cognitive and Physical Performance in Women Presenting with Heavy Menstrual Bleeding: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Heavy menstrual bleeding is a common gynecological problem and a leading cause of iron deficiency anemia in women of reproductive age. While anemia is routinely identified, its impact on cognitive function and physical performance is frequently under-recognized in clinical practice. Objective: To determine the association of iron deficiency anemia with cognitive and physical performance in women presenting with heavy menstrual bleeding. Methods: This hospital-based cross-sectional study was conducted from February 2024 to March 2025 at a tertiary-care hospital. A total of 120 women aged 18–45 years presenting with heavy menstrual bleeding were enrolled through consecutive sampling. Hemoglobin and serum ferritin levels were measured to classify participants into iron deficiency anemia and non–iron deficiency anemia groups. Cognitive performance was assessed using the Montreal Cognitive Assessment, while physical performance was evaluated using handgrip strength and the six-minute walk test. Statistical comparisons and multivariable regression analyses were performed to assess independent associations. Results: Iron deficiency anemia was present in 58.3% of participants. Women with iron deficiency anemia demonstrated significantly lower cognitive scores and reduced physical performance compared to non-anemic women. Hemoglobin and ferritin levels showed positive correlations with both cognitive and physical performance measures. After adjustment for potential confounders, iron deficiency anemia remained an independent predictor of impaired cognitive function and reduced physical capacity. Conclusion: Iron deficiency anemia is highly prevalent among women with heavy menstrual bleeding and is independently associated with poorer cognitive and physical performance. Routine screening and timely management of iron deficiency anemia should be integrated into standard care for women with heavy menstrual bleeding to improve functional outcomes.
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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 | 0.000 | 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".