Optimizing care for women experiencing pregnancy loss: Insights from a systematic review and meta-analysis
Bibliographic record
Abstract
Title: Optimizing Care for Women Experiencing Pregnancy Loss: Insights from a Systematic Review and Meta-Analysis This systematic review and meta-analysis was developed to comprehensively assess interventions targeting clinical and psychological outcomes for women experiencing pregnancy loss. The PRISMA checklist ensures the study adheres to rigorous standards for transparency, reproducibility, and methodological integrity. Below is a detailed account of how each checklist item was addressed in this research. Title and Registration (Items 1–2) The title of the study explicitly identifies it as a systematic review and meta-analysis, signaling the methodology employed. The study was registered with PROSPERO under the ID CRD42025635112, ensuring public accessibility and accountability. This registration includes a detailed protocol that outlines the study’s objectives, eligibility criteria, and planned analyses, safeguarding transparency. Authors and Contributions (Items 3a–3b) The authors of this study are Dr. Prieyadharshini Jayaprakasam (Mid Yorkshire NHS Trust, UK), Dr. Jeyaram Nadarajan Saraswathy (Government Vellore Medical College, India), and Dr. Arbind Kumar Choudhary (Government Erode Medical College, India). Dr. Jayaprakasam conceptualized the study and designed the framework. Dr. Saraswathy was responsible for data collection, screening, and quality assessment of studies, while Dr. Choudhary led the statistical analyses and manuscript preparation. The diverse expertise of the team ensures methodological rigor and practical relevance. Rationale and Objectives (Items 7–8) The rationale for this study stems from the significant clinical and psychological burden caused by pregnancy loss, which affects millions of women globally. Interventions such as pharmacological therapies, psychological support, and assisted reproductive technologies (ART) hold promise for improving outcomes but require comprehensive evaluation. The objective was to assess the effectiveness of these interventions using a systematic and evidence-based approach. Eligibility Criteria and Information Sources (Items 9–10) Eligibility criteria were clearly defined to include studies published between 2020 and 2024 that evaluated interventions for pregnancy loss. Non-English studies and those with insufficient data were excluded to maintain focus and ensure data reliability. Information sources included PubMed, Cochrane CENTRAL, EMBASE, CINAHL, Scopus, and manual citation searches, ensuring a comprehensive literature review. Search Strategy and Study Records (Items 11–12) The search strategy was tailored for each database and included terms such as “pregnancy loss,” “ART interventions,” “mifepristone,” “misoprostol,” and “mindfulness therapy.” The search strategy was detailed in the protocol to enable reproducibility. All study records were systematically managed using Mendeley reference management software, where duplicates were identified and removed. Selection and Data Collection Processes (Items 13a–13b) The study selection process involved two independent reviewers screening titles, abstracts, and full texts against predefined criteria. Any discrepancies were resolved by a third reviewer to minimize bias. Data were extracted using a standardized template that included intervention details, outcomes, and population characteristics. This template ensured consistency in data collection and facilitated comprehensive analysis. Data Items and Outcomes (Items 14–15) The primary outcomes of interest were clinical pregnancy and live birth rates. Secondary outcomes included psychological measures such as stress reduction, patient satisfaction, and mental health improvements. Data on tissue expulsion rates and adverse effects were also extracted for pharmacological interventions. These outcomes were chosen to address both physical and emotional aspects of pregnancy loss. Risk of Bias and Data Synthesis (Items 16–17) The risk of bias was assessed using the Cochrane Risk of Bias tool for randomized controlled trials (RCTs) and the Newcastle-Ottawa Scale for observational studies. Meta-analysis was conducted using Review Manager (RevMan), with heterogeneity assessed via the I² statistic. Subgroup analyses explored variations in outcomes based on pregnancy loss types, geographic regions, and healthcare settings. Sensitivity analyses were performed to evaluate the robustness of the results. Meta-Biases and Confidence in Evidence (Items 18–19) Publication bias was assessed using funnel plots and Egger’s regression test. No significant publication bias was detected. The GRADE framework was applied to evaluate the quality of evidence for each outcome, providing confidence in the reliability of findings. Reporting and Dissemination Plans (Items 20–21) The study was reported in accordance with PRISMA guidelines, with dedicated sections for methods, results, discussion, and conclusions. Findings will be disseminated through peer-reviewed journal publications and presentations at obstetrics and gynecology conferences, ensuring widespread accessibility. Timelines and Ethics (Items 22–23) The study was conducted between 2020 and 2024, with publication planned for 2025. Ethical approval was not required, as the study exclusively utilized published data and did not involve human or animal participants. Amendments and Accessibility (Items 24–25) Any amendments to the protocol will be recorded in PROSPERO and detailed in future publications. The protocol and extended data, including the PRISMA-P checklist, analyzed figures, and data templates, are publicly accessible on Zenodo (DOI: 10.5281/zenodo.14679477). Review Team and Funding (Items 26–27) The review team comprises experts in obstetrics (Dr. Jayaprakasam), gynecology (Dr. Saraswathy), and pharmacology (Dr. Choudhary). No external funding or sponsorship was received; the study was independently conducted by the authors.
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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 | 0.091 | 0.252 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.017 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".