Newcastle-Ottawa Quality Assessment Scale.
Bibliographic record
Abstract
<div> Background One of the main factors contributing to maternal morbidity and mortality is induced abortion. The WHO estimates that over 44 million induced abortions take place annually around the world. The majority of these abortions—about 50%—are unsafe, significantly increasing maternal morbidity and contributing to 13% of maternal deaths. Thus, this review aimed to estimate the pooled prevalence of induced abortion and its associated factors in Africa. Methods To find literature on the prevalence of induced abortion and its associated factors, a thorough search of the internet databases such as PubMed/MEDLINE, African Journals Online, and Google Scholar was conducted. The data were extracted using a structured method of data collection. Software called STATA 14 was used to do the analysis. funnel plot and Egger regression test were used to evaluate potential publication bias. I<sup>2</sup> statistics and Cochrane’s <i>Q</i> were used to measure the heterogeneity at a p-value < 0.05. Results 976 studies were found through a thorough search of electronic databases. Finally, 46 full-text abstract papers were included in this study. The estimated pooled prevalence of induced abortion was 16% (95% CI: 13%-19%). According to the sub-group analysis, most studies were conducted in Ethiopia, and the pooled prevalence was 19% (95% CI: 10%–30%). Similarly, the subgroup analysis by year of study showed that the prevalence of induced abortion was 39% (95% CI: 17%–64%) among studies conducted in 2019. Conclusion The results of this study thus imply that the pooled prevalence of induced abortion is higher than that of earlier studies that were published in some nations. the data from this study are needed to support reproductive and adolescent health programmers and policymakers and to formulate recommendations for future clinical practice and guidelines. </div>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.149 | 0.004 |
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; both teacher heads agree on what is shown here.
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".