Exploring the effect of menstrual loss and dietary habits on iron deficiency in teenagers: A cross-sectional study
Bibliographic record
Abstract
Adolescent girls are particularly susceptible to iron deficiency due to increased iron requirements during the pubertal growth spurt, combined with iron loss following menarche. This study aimed to investigate the prevalence of heavy menstrual bleeding in an adolescent population using the SAMANTA questionnaire and to explore its relationship with dietary habits and iron deficiency. This cross-sectional study was conducted in two Swedish high schools in 2023. Post-menarchal female students, aged 15 and older, were included (n = 394). Data were collected on-site through a patient-reported survey, including the SAMANTA questionnaire for heavy menstrual bleeding, and by blood sampling. Meat-restricted diet was analyzed in relation to iron status. Descriptive analysis and regression analysis were used to assess the prevalence of heavy menstrual bleeding and its relationship with dietary habits and iron deficiency, defined as ferritin <15 µg/L. The prevalence of heavy menstrual bleeding and iron deficiency in the cohort was 53% (208/394) and 40% (157/394), respectively. In univariate analysis, heavy menstrual bleeding (OR 3.0, 95% CI [2.0, 4.6]) and a meat-restricted diet (OR 3.5, 95% CI [2.2, 5.6]) were both associated with increased odds of iron deficiency. When assessing the joint effect of having heavy menstrual bleeding and a meat-restricted diet, the odds of iron deficiency were 13.5 times higher compared to omnivore individuals with normal menstruation (OR 13.5, 95% CI [6.4, 28.7]). Overall, the prevalence of iron deficiency in this population of adolescent girls was very high. Heavy menstrual bleeding and a meat-restricted diet were both independently associated with increased odds of iron deficiency. However, odds for iron deficiency were monumentally higher when combining these two variables, thus highlighting the importance of assessing and addressing both excessive output and low intake of iron.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".