High prevalence of undiagnosed iron deficiency in endometriosis patients: A cross‐sectional study
Bibliographic record
Abstract
OBJECTIVE: The primary objective was to evaluate the prevalence of undiagnosed iron deficiency in patients with endometriosis. METHODS: We performed a multi-center, cross-sectional study at two tertiary care hospitals. We included 251 non-pregnant women (18-50 years old) presenting with a clinical or surgical diagnosis of symptomatic endometriosis. Patients who consented to the study underwent screening bloodwork (including complete blood count, ferritin, and transferrin saturation) and completed the study survey assessing demographics, medical and surgical history, and validated questionnaires to assess iron deficiency and endometriosis symptoms. RESULTS: The prevalence of iron deficiency in our endometriosis cohort was 53.4% (134/251), and the prevalence of iron deficiency anemia was 13.5% (34/251). Patients with iron deficiency were more likely to have heavy menstrual bleeding (HMB) compared with patients without iron deficiency (66/133, 49.6% vs. 40/115, 34.8%, p = 0.022). Nonetheless, 58% (142/251) of our study population did not endorse HMB. Despite absence of HMB, 47% (67/142) of these patients were iron-deficient. Transferrin saturation was diagnostic for iron deficiency in 63 of 176 patients (35.7%) who had a normal ferritin (≥30 ng/mL). Patients with iron deficiency had a significantly lower adjusted median Functional Assessment of Chronic Illness Therapy Fatigue Subscale score compared with those without iron deficiency (26.3. vs. 29.8, p = 0.025). CONCLUSION: This study highlights the high prevalence of iron deficiency, which remains undiagnosed in over half of patients with endometriosis presenting to a gynecologist. Future research should focus on assessing the effectiveness of iron therapy in improving symptoms and overall well-being in this population.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".