The association between anemia and depression in older adults and the role of treating anemia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: To investigate the association between anemia and depression and whether the treatment of anemia modifies the effect of the association between anemia and depression. METHODS: This secondary data analysis is based on data from the Enquête sur la santé des aînés (ESA)-Services study conducted in 2011-2013 on community-dwelling older adults recruited in primary care and have given access to their medico-administrative data (n = 1447). The presence of anemia was self-reported, as was depression (major and minor) aligned with symptoms of the DSM-5. Treated anemia was based on the presence of medications delivered to participants. Cross-sectional associations were analyzed using multivariable logistic regression, controlling for confounders. RESULTS: The prevalence of self-reported anemia in our sample was estimated at 6.7%. Self-reported anemia was associated with increased odds of depression. Individuals with untreated anemia had a 2.6-fold increased odds of depression compared to those with no anemia. In contrast, the odds of depression in individuals with treated anemia were not different from individuals with no anemia. CONCLUSION: The findings underline the importance of treating anemia in older adults. Future longitudinal studies are needed to replicate the findings and further explore the role of treating anemia on symptoms of depression.
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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 it