Ultrasound imaging and the culture of pregnancy management in low‐and middle‐income countries: A systematic review
Bibliographic record
Abstract
BACKGROUND: Obstetric ultrasound imaging is a relatively new, but rapidly expanding, technology in low- and middle-income countries (LMICs). Given that new technologies modify practices, the influence of ultrasound on pregnancy management in LMICs is not comprehensively understood. OBJECTIVES: To map how ultrasound technology may be modulating the culture of pregnancy management in LMICs. SEARCH STRATEGY: A search of five databases up to November 18, 2022. SELECTION CRITERIA: Original, peer-reviewed articles from LMICs, published in English from 2000 to 2022. DATA COLLECTION AND ANALYSIS: All articles were assessed for quality using the GRADE approach. Data were analyzed thematically to generate new interpretive constructs and explanations. RESULTS: Forty articles involving 113 000 respondents suggests that obstetric ultrasound is becoming the preferred method of pregnancy surveillance, replacing clinically important components of prenatal care. Mothers overestimate ultrasound as an all-powerful diagnostic and "therapeutic" tool that can deliver the perfect baby. For-profit providers are driving medically unnecessary scans while the poor do not receive the recommended scans. CONCLUSION: Ultrasound technology has modified the culture of pregnancy management in LMICs in unintended and possibly harmful ways. Private health services are pushing the detrimental trends. Limitations include generalizability of qualitative studies and insufficient attention to inequities.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".