Factors Affecting Improved Prenatal Screening: A Narrative Review
Bibliographic record
Abstract
BACKGROUND: Prenatal screening deals with the detection of structural and functional abnormalities in the fetus. Health care providers can minimize unintended pregnancy outcomes by providing proper counseling and performing prenatal screening. The purpose of the present review study is to investigate factors affecting improved prenatal screening. METHODS: The present study is a narrative review searching public databases such as Google Scholar and specialized databases such as Pubmed, Magiran, Scientific Information Database, Elsevier, Ovid and Science Direct as well. Using the keywords "prenatal screening", "fetus health" and "prenatal counseling", 70 relevant articles published from 1994 to 2014 were selected. After reviewing the abstracts, the full data from 26 articles were ultimately used for writing the present review study. RESULTS: Three general themes emerged from reviewing the studies: health care providers' skills, clients' characteristics and ethical considerations, which were the main factors affecting improved prenatal screening. CONCLUSION: Prenatal screening can be successful if performed by a trained and experienced expert through techniques suitable for the mother's age. Also simultaneously providing proper counseling and giving a full description of the risks and benefits of the procedures for clients is recommended.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".