Prevalence and determinants of iron depletion and anemia among Canadian Inuit
Bibliographic record
Abstract
Accelerated loss of traditional lifestyles may place Inuit at risk of iron depletion, given that anemia has been frequently observed. Study objectives were to determine the prevalence of anemia, storage iron depletion and iron overload; and to identify correlates of iron status in Canadian Inuit adults. In a cross‐sectional survey of 2550 adults, hemoglobin, serum ferritin, soluble transferrin receptor (on a subset), and C‐reactive protein (CRP) were measured on fasting venous blood. Anthropometry, dietary, sociodemographic and health data were collected. Correlates of iron status were assessed with multivariate linear and logistics models. Low prevalence of inadequate iron intake was observed (<10 %). For men with CRP<10 mg/L (n=804) 6.5 % had depleted and 10.3 % had elevated iron stores. For women with CRP<10 mg/L (n=1260) 29.4 % had depleted iron stores. Anemia was present in 16.1 % of men and 21.7 % of women. Iron depletion explained 51 % of anemia cases in women but few cases in men (15 %). Odds ratios for iron depletion were 2.3 (1.2–4.6 95% CI) for food insecure men and 2.1 (1.0–4.1) for men without a household hunter. Among food insecure women, higher n3‐PUFA status was associated with reduced risk of iron depletion (OR=0.84; 0.72–0.98). Iron depletion is a concern for young Canadian Inuit women. Interventions should address food security and traditional food access. Funding: IPY, HC, CIHR, INA, GNHSS, ArcticNet.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".