Iron Deficiency, Iron Deficiency Anemia, and Infectious Disease in Calgary, Alberta
Bibliographic record
Abstract
An estimated 30% of the global population suffers from iron deficiency anemia (IDA) and previous studies have suggested that iron deficiency (ID) and IDA are associated with adverse health outcomes. However, some research suggests that ID and IDA may be adaptive in areas with high levels of endemic infectious diseases. The present study examined the association between serum iron levels and four infectious diseases in a sample of 55,437 individuals in Calgary, Alberta. Associations between sociodemographic variables (SDVs) and iron and infection were also tested to explore ID’s complex etiology. This study evaluates two hypotheses: HA1: Low baseline serum iron predicts a lower risk of infection up to one year out, and HA2: Sociodemographic variables (higher median income, more postsecondary education, non-immigrant status, and non-Indigenous status) will be associated with higher serum iron levels. Cox regression analyses found that the lowest levels of iron were predictive of greater risk for infection in sepsis (blood) and urinary tract infections. Iron level was not associated with fungal sepsis and strep throat. Multiple regression analyses found no significant relationships between infection and SDVs and found that greater median household income and postsecondary education level were associated with higher mean serum iron levels. This research emphasizes the importance of context when evaluating the adaptiveness of a trait, in addition to calling for further investigation into individual-level associations of sociodemographic variables and infection throughout the city of Calgary.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".