Diagnostic Accuracy of Grey Ultrasound in Antenatal Diagnosis of Morbidly Adherent Placenta, Taking Per-Operative Findings of Caesarean Section as Gold Standard
Bibliographic record
Abstract
Objective: To assess the diagnostic accurateness of grey ultrasound in the antenatal diagnosis of morbidly adherent placenta, taking per- operative findings of caesarean section as gold standard. Study Design: Cross sectional study. Place and Duration of Study: Radiology Department in collaboration with Obstetrics and Gynecology Department, Pak Emirates Military Hospital, Rawalpindi Pakistan, from Aug 2019 to Aug 2020. Methodology: 89 pregnant women registered with diagnosis of placenta Previa in present pregnancy were included. Primi-gravida and females with pregnancies complicated by diabetes, heart disease and hypertension were omitted from study. All patients endured grey ultrasound examination and revealed absence or presence of morbidly adherent placenta Previa. Grey ultrasound results were associated with surgical per-operative findings. Results: The mean age of the patients was 27.78±2.65 years. In patients with positive ultrasound results, 01 was false positive and 10 were true positive. Of the 78 ultrasound negative results, true negative were 70 patients and 02 were false negative (p=1000). Of the 78 ultrasound negative results, true negative were 70 patients and 02 were false negative (p=1000). Specificity, sensitivity, negative predictive value, positive predictive value and diagnostic accurateness of grey ultrasound in the antenatal analysis of morbidly adherent placenta are 98.36%, 87.5%, 98.36%, 87.5% and 97.10%, respectively. Conclusion: In this study, the grey ultrasonography showed greater diagnostic accuracy in the prenatal detection of the morbidly adherent placenta in pregnant women with placenta previa.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".