Standardization and quality control of Doppler and fetal biometric ultrasound measurements in low‐income setting
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine the quality of fetal biometry and pulsed-wave Doppler ultrasound measurements in a prospective cohort study in Uganda. METHODS: This was an ancillary study of the Ending Preventable Stillbirths by Improving Diagnosis of Babies at Risk (EPID) project, in which women enroled in early pregnancy underwent Doppler and fetal biometric assessment at 32-40 weeks of gestation. Sonographers undertook 6 weeks of training followed by onsite refresher training and audit exercises. A total of 125 images for each of the umbilical artery (UA), fetal middle cerebral artery (MCA), left and right uterine arteries (UtA), head circumference (HC), abdominal circumference (AC) and femur length (FL) were selected randomly from the EPID study database and evaluated independently by two experts in a blinded fashion using objective scoring criteria. Inter-rater agreement was assessed using modified Fleiss' kappa for nominal variables and systematic errors were explored using quantile-quantile (Q-Q) plots. RESULTS: For Doppler measurements, 96.8% of the UA images, 84.8% of the MCA images and 93.6% of the right UtA images were classified as of acceptable quality by both reviewers. For fetal biometry, 96.0% of the HC images, 96.0% of the AC images and 88.0% of the FL images were considered acceptable by both reviewers. The kappa values for inter-rater reliability of quality assessment were 0.94 (95% CI, 0.87-0.99) for the UA, 0.71 (95% CI, 0.58-0.82) for the MCA, 0.87 (95% CI, 0.78-0.95) for the right UtA, 0.94 (95% CI, 0.87-0.98) for the HC, 0.93 (95% CI, 0.87-0.98) for the AC and 0.78 (95% CI, 0.66-0.88) for the FL measurements. The Q-Q plots indicated no influence of systematic bias in the measurements. CONCLUSIONS: Training local healthcare providers to perform Doppler ultrasound, and implementing quality control systems and audits using objective scoring tools in clinical and research settings, is feasible in low- and middle-income countries. Although we did not assess the impact of in-service retraining offered to practitioners deviating from prescribed standards, such interventions should enhance the quality of ultrasound measurements and should be investigated in future studies. © 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
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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 | 0.084 | 0.154 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".