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Record W4406407928 · doi:10.1101/2025.01.15.25320586

Maternal Serum Hemoglobin Levels Relative to Anemia Interventions in the COVID-19 era: A systematic review and <i>meta</i> -analysis

2025· review· en· W4406407928 on OpenAlexaff
John Kyalo Muthuka, Muthoni L. Agnes, Chembugei C. Lucy, Mbari F. Dianna, Rosemary Nabaweesi

Bibliographic record

VenuemedRxiv · 2025
Typereview
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsAlberta Health Services
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Meta-analysisPsychological interventionHemoglobinAnemiaMedicinePandemic2019-20 coronavirus outbreakVirologyIntensive care medicineInternal medicineDisease

Abstract

fetched live from OpenAlex

Abstract Aims The purpose of this review was to investigate the effect of different types of maternal anemia interventions on serum hemoglobin levels among pregnant cohort of reproductive age during COVID-19 pandemic using a pool of studies conducted with different research designs. Methods Relevant studies were identified from January 2020 to December 2022 by using MeSH terms in PubMed, Embase, Cochrane Library, Clinical trials, Scopus databases and gray literature. The studies were systematically reviewed, and quality assessments were evaluated by the guidelines of the Cochrane risk of bias. All the statistical analysis was performed on RevMan 5.4.1 software with a random and fixed effect models. Heterogeneity was evaluated with the Cochran Q statistic and Higgins test and publication bias assessed via funnel plots. The cumulative pooled effect of maternal anemia interventions ( n = 15) on serum hemoglobin concentration was the main outcome, while effects of specific types of maternal anemia interventions; mode of administration; and the country a study was conducted were derived as secondary outcomes. In total, 6695 pregnant women of reproductive age were established from the entire pool of studies. Mean serum hemoglobin levels’ data were retrieved alongside their standard errors for ascertaining the effect of the said maternal anemia interventions on hemoglobin levels using f standard mean and mean differences during meta-analysis. The review component was part of registered PROSPERO: CRD-CRD42023410657:[ Results Fifteen studies met the inclusion criteria, with the current study findings proposing that, maternal anemia interventions in overall improved serum hemoglobin levels [SMD (95% CI) = 0.93 [0.57, 1.30]; Heterogeneity: Tau-squared = 0.50; Chi-squared = 627.07, df = 14 (P < 0.00001); I-squared = 98%. The effect of specific intervention categories was; dietary iron supplement 1.16 [95% CI = −0.97, 3.28], education information 0.75 [95% CI = 0.12, 1.37], Intravenous ferric carboxy-maltose 1.33 [95% CI = 0.28, 2.38], intravenous iron sucrose 0.82 [95% CI = 0.56, 1.09] and herbal substances 0.42 [95% CI = 0.24, 0.61]. Dietary iron supplementation didn’t demonstrate an improvement on hemoglobin levels (Test for overall effect: Z = 1.07 (P = 0.29). Parenterally given and via education administered interventions had better utility on hemoglobin concentrations with maternal anemia interventions among developing countries showing insignificant effect on maternal serum hemoglobin levels. Conclusion Current review concluded that maternal anemia interventions had a utility on serum hemoglobin, more feasible, those given as injections. Notably, dietary iron interventions were compromised especially in developing nations during COVID-19 pandemic. Multifaceted further clinical studies are crucial to substantiate current findings.

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 imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.048
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0180.030
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.096
GPT teacher head0.398
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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