<i>Retracted</i> : Virtual clinic in pregnancy and postpartum healthcare: A systematic review
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Background and Aims: To monitor the health status of pregnant women moment by moment, new technologies in the field of telemedicine can be used, such as virtual visits and virtual clinics. During the COVID-19 pandemic, by using these technologies, useful and satisfactory services have been provided to pregnant mothers. The aim of this study is to specify the applications, features, and infrastructure of a comprehensive virtual clinic in the field of gynecological and pregnancy care. Methods: A systematic review search was conducted through the scientific databases from February 2013 to February 2022 using Scopus, Web of Science, and PubMed. Furthermore, manual searches in Google Scholar and the reference lists of included studies were carried out. Results: In this systematic review we included 16 articles that reported experiences in virtual clinics in pregnancy and postpartum healthcare. The involved studies were experimental, cohort, and cross-sectional studies. The target group users were pregnant or women who gave birth and families of neonatal. The application of virtual clinics was for the visit, consultation, monitoring, follow-up, and home care virtually. Highly satisfaction scores of caregivers after virtual visits and consultation were reported. There were some challenges during virtual visits and consultation; the most important challenge was a poor internet connection. Conclusion: The reviewed studies show promising outcomes according to patient and provider satisfaction. We predict that telehealth will become a growingly significant part of gynecological care in the future.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".