Development and validation of the placenta-QUS model for the detection of placenta-mediated diseases using quantitative ultrasound measurements: An Ex Vivo proof-of-concept study
Bibliographic record
Abstract
INTRODUCTION: Placenta-mediated diseases are associated with structural changes in the placenta. Quantitative Ultrasound (QUS) imaging measures the acoustic properties of the tissue, which are correlated to the underlying tissue structure. We aimed to develop and validate a diagnostic prediction model using QUS measurements for pre-eclampsia (PE) and small-for-gestational-age (SGA) fetuses/neonates. METHODS: For this prospective case-control study, placentas were collected from a group of women who delivered via cesarean section at BC Women's Hospital, Vancouver, Canada. Ultrasound data were collected and processed to compute three QUS parameters, namely, attenuation coefficient estimate (ACE), integrated backscatter coefficient (IBC), and effective scatterer diameter (ESD) from the placentas. We developed a logistic regression model using QUS parameters as predictors. The primary outcome was the occurrence of SGA and PE. RESULTS: The dataset consisted of 47 placentas, of which 25 placentas were complicated by SGA/PE. The final placenta-QUS model included quadratic and interaction terms of ACE, IBC, and ESD parameters. The placenta-QUS model was well-calibrated, with a calibration slope of 0.99 (0.57-1.05) and a calibration intercept of 0.003 (-0.02 - 0.22). The model predicted the SGA/PE complicated pregnancies with an apparent Area Under the Receive Operating Characteristic Curve (AUROC) of 0.89 (95 % CI: 0.78-0.98). The optimism-adjusted AUROC was 0.88 (95 % CI: 0.78-0.98). DISCUSSION: A model for SGA and PE has been developed using QUS measures from the placenta ex vivo. The model showed promising performance in detecting SGA/PE. Future studies will be performed to assess the model performance using QUS measures in utero.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".