Effects of Iron Therapy on the reduction of anemia among women of reproductive age in developing countries: A protocol for systematic review and Meta-analysis
Bibliographic record
Abstract
Abstract Background Iron deficiency anemia is a major public health issue among women of reproductive age (WRA), as it leads to high maternal and infant morbidity and mortality. The WHO recommends the distribution of iron supplements to all women in populations in which the prevalence of anemia (in women) exceeds 20%. Interventional studies have been carried out in developing countries regarding the efficacy and safety of iron in reducing anemia among women of reproductive age. However, the findings from randomized controlled trials have not been systematically synthesized. Therefore, we performed a systematic review and meta-analysis of RCTs to both qualitatively and quantitatively assess the evidence for the efficacy and effectiveness of iron as an intervention for reducing anemia among women of reproductive age in developing countries. Methods This review and meta-analysis will focus on assessing the efficacy and effectiveness of iron as an intervention for reducing anemia among women of reproductive age. PubMed, Google Scholar, and Science Direct will be explored using a detailed search strategy. A study will be considered eligible if it is a randomized controlled trial determining the efficacy and effectiveness of iron in anemia reduction among WRA (15-49 years) in developing countries and published in the English language in a peer-reviewed local or international journal from 2000 to 2020. We will perform a meta-analysis to measure the effect of individual studies as well as the pooled effect of all studies by generating forest plots. Heterogeneity will be assessed using I 2 statistics and a funnel plot will be developed to assess the publication bias. Discussion This systematic review will provide an opportunity to synthesize and critically appraise the findings from individual studies on the efficacy and effectiveness of iron as an intervention for reducing anemia among women of reproductive age to generate a comprehensive summary. These aggregated findings, in turn, can guide clinical practice and help clinicians, researchers and policymakers make evidence-based decisions to address this pressing health concern among women of reproductive age residing in developing countries. The findings of the review will be disseminated through presentations and peer-reviewed publications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 | 0.080 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.022 | 0.037 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.003 |
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".