P03.01: Validation of a clinical and sonographic‐based scoring system for prenatal prediction of morbidly adherent placenta in high‐risk population
Bibliographic record
Abstract
To validate three sonographic-based scoring system in prediction of morbidly adherent placenta (MAP) in high-risk population. Retrospective cohort study was conducted including pregnant women with previous uterine scar (Caesarean section, dilatation and curettage, etc.) and anterior placenta (previa or not) who had Ultrasound evaluation and delivered in our centre. Using three previously proposed sonographic-based score system published by Tovbin et al, Rac et al, and Gilboa et al to predict MAP, Ultrasound images performed in our unit were reviewed by a junior Maternal Fetal Medicine Specialist blinded from the final pathology report and/or surgical notes. Parameters assessed by Tovbin included number of previous Caesarean sections, number and size of placental lacunae, obliteration of demarcation between uterus and placenta, location of the placenta and Doppler assessment. Rac assessed two or more previous Caesarean sections, Lacunae grade, sagittal smallest myometrial thickness, anterior placenta and bridging vessel. Finally, Gilboa evaluated presence and number of placental lacunae, interruption of the uterus-bladder interface, obliteration of demarcation between the uterus and the placenta. 55 pregnant women who met the inclusion criteria were reviewed. Nine (16%) cases of MAP were found at the time of delivery and all had hysterectomy. Pathology report confirmed operative finding. Scoring systems designed by Tovbin et al, Rac et al and Gilboa et al identified 88%, 77% and 55% of cases of MAP, respectively. When the scoring system ruled out MAP, Tovbin predicted 97%, Rac 95% and Gilboa 97%. In our cohort study, Tovbin had superior prediction of MAP than the other two scoring system. No difference was found to prediction negative cases of MAP for all three scoring system and appear to be a useful tool. Larger sample and further statistical analysis should be performed before implementation of scoring system in a routine high risk patient assessment.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".