Quantitative ultrasound in obstetrics and perinatal care
Bibliographic record
Abstract
This talk will present recent results from our multi-institutional effort to advance the application of Quantitative Ultrasound in obstetrics and in perinatal care. In the field of obstetrics, we are investigating the use of shear wave elasticity imaging (SWEI) to predict the risk of preterm birth based on assessing the viscoelastic properties of the uterine cervix. Longitudinal studies in non-human primates (NHP) and humans showed a similar rate of decrease of the shear wave speed, SWS (1.6% and 1.5%/% gestational age, respectively), validating the animal model and indicating the value of SWS change as a biomarker for cervical softening. We are currently optimizing the analysis of shear wave dispersion to assess changes in viscosity. In the field of perinatal care, results from a pilot study in NHP neonates suggest that the effective size of diffuse scatterers can be used as a biomarker for thalamic apoptosis induced by long exposures to anesthesia. We are currently investigating the use of coherent scattering features to increase the sensitivity and specificity to neuroapoptosis and to improve attenuation and backscatter coefficient estimations in complex tissues such as the cervix and the neonate brain. [Acknowledgments: All protocols have been IRB- and/or IACUC-approved, and are HIPAA compliant. Equipment loan and technical support from Siemens Healthcare. Funded by: NIH Grant Nos. T32CA009206, F31HD082911, R01HD072077, UL1TR000427, P51OD011106, and R01HD083001; NSERC Grant No. RGPIN-2015-04136; and UNAM-PAPIIT IA104518 and IN107916.
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".