Relationship of Zinc and Magnesium Serum Levels with Postpartum Depression in Tabriz-Iran
Bibliographic record
Abstract
OBJECTIVE: According to the World Health Organization, depression will be the second prevalent problem after ischemic heart diseases by the year 2020. Postpartum depression (PPD) as a major depressive episode has devastating impacts on the health of mother, newborn, infant, and even the whole family. This study was conducted to investigate the relationship of zinc and magnesium serum levels with PPD, as one of the commonly assumed causes of depression. METHODS: This cross-sectional study was done on 122 postpartum women aged 18 years and more in two educational hospitals and one non-educational hospital in Tabriz-Iran, 2015. The eligible women were selected using convenience sampling method. Then, the demographic characteristics questionnaire and Edinburgh Depression Scale were completed by participants, and 5cc of blood sample was drawn from each participant. For data analysis, logistic regression test was used. RESULTS: The mean score of depression scale was 8.0 (SD: 4.7), meaning that 18.9% of mothers were depressed. Results indicated a significant inverse correlation between Edinburgh depression score and magnesium serum level (p= 0.001). However, there was no statistically significant relationship between the zinc serum level and Edinburgh depression score (p=0.831), in so far as based on logistic regression analysis, increased magnesium serum level decreased the odds of depression [Odds ratio: 0.05; CI 95%: 0.01 to 0.29]. CONCLUSIONS: In this study, there was a significant inverse relationship between magnesium serum level and Edinburgh depression score.
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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 | 0.001 |
| 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) | 0.001 | 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".