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Record W4387210064

Meta Analysis of the Effects of Thalassemia Minor on the Incidence of Amniotic Fluid Abnormalities and the Amount of Bleeding During Delivery.

2024· article· en· W4387210064 on OpenAlexaboutno aff
Yan Huang, Ting Zhang, Honglin Wang, Huili Chen, Zhaoli Chen

Bibliographic record

VenuePubMed · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicReproductive System and Pregnancy
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineObstetricsAmniotic fluidPregnancyOligohydramniosIncidence (geometry)ThalassemiaGynecologyFetusInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Objective: To analyze the effects of thalassemia minor on the incidence of amniotic fluid abnormalities and the blood loss of pregnant women during delivery based on the database. Methods: PubMed, EMBASE, EBSCO, Web of Knowledge and Ovid databases were searched for articles on the incidence of amniotic fluid abnormalities and the amount of bleeding during delivery in pregnant women with mild thalassemia; it can also be combined with manual retrieval for literature review. The data retrieval period was from the establishment of the database to June 2022. According to the Newcastle Ottawa scale score, the quality of the six included literature was evaluated, and the Revman processing software was used for meta-analysis. Results: The 6 included articles are all high-quality literature, including 364 cases in the case group and 689 cases in the control group. The publication years of the literature are mainly from 2013 to 2021, and they are all high-quality literature. All literature was blinded, and a total of 4 pregnancy outcomes were extracted from the 6 included literature, including oligohydramnios/oligohydramnios, postpartum hemorrhage, preterm delivery, and cesarean section. Compared to normal pregnant women, the level of postpartum bleeding in thalassemia pregnant women was significantly increased [RR = 2.40, 95% CI (1.63-3.54), P < .05], and the difference was statistically significant. Compared to normal pregnant women, thalassemia pregnant women have a significantly higher risk of developing excessive/insufficient amniotic fluid [RR = 2.71, 95% CI (2.52-2.81), P < .01], and the difference is statistically significant. Compared to normal pregnant women, pregnant women with thalassemia have a significantly higher risk of premature birth [RR = 3.02, 95% CI (1.84~4.96), P < .05], and the difference is statistically significant. Compared to normal pregnant women, the risk of cesarean section in thalassemia pregnant women is significantly increased [RR = 1.68, 95% CI (1.39-2.02), P < .05], and the difference is statistically significant. Conclusion: Thalassemia minor can increase the incidence of amniotic fluid abnormalities and the amount of bleeding during labor. In the future, we should strengthen the health education of pregnant women, improve the understanding of the disease, avoid or reduce the impact of thalassemia on newborns, improve the pregnancy outcome, and provide a more reliable basis for clinical decision-making.However, there are still certain limitations: (1) the literature selected in the study for the past 5 years is relatively small, and they are all single center, retrospective studies, and have a small sample size, resulting in insufficient accuracy of the results of the meta-analysis; (2) Some literature lacks blind methods, which may lead to language bias and implementation bias in the results; (3) The research time is still short, and it has not been clear how different types of thalassemia affect abnormal amniotic fluid volume and postpartum bleeding.

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)0.015
metaresearch head score (Gemma)0.029
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: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.029
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0140.048
Bibliometrics0.0060.005
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.203
Teacher spread0.187 · 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
GenreEmpirical

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
Published2024
Admission routes1
Has abstractyes

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