Diagnostic Accuracy of Cerebroplacental Ratio in Prediction of Postnatal Outcomes in Oligohydramnios
Bibliographic record
Abstract
BACKGROUND AND AIM: The evidence on isolated oligohydramnios (IO) patients and their postnatal outcomes are inconsistent. Recent research has clarified the connection between that IO and negative outcomes in the postnatal period. Our goal was to analyze the correlation between Doppler measurements and postnatal outcomes in oligohydramnios patients, with a focus on the cerebroplacental ratio (CPR). METHODOLOGY: A cohort study was conducted in the Radiology Department of Khan Research Laboratories (KRL) Hospital from October 2021 to July 2022. One hundred women were chosen as the sample size. For this study, we used the Raosoft sample size calculator with a 95% confidence interval and a 5% margin of error. Both the middle cerebral artery and the umbilical artery were imaged using ultrasound, and the systolic-to-diastolic ratio and peak systolic velocity are recorded. Pulsatility index (PI) and resistive index (RI) were also calculated. If the amniotic fluid index (AFI) is less than 5 cm, the condition is known as oligohydramnios. The newborn's APGAR score was taken immediately after birth as well as after 5 minutes. RESULTS: We have determined that, on average, mothers are 35.45 weeks/248.15 days pregnant. When compared to the reference standard, CPR diagnostic features showed a sensitivity of 92% and a specificity of 77.27. Overall diagnostic accuracy is predicted to be 93.0%, with a 93.50% positive prognosis and a 73.91% negative prognosis. The effect size for the change in APGAR scores before and after the test was -2.38 1.03, with a 95% confidence interval of -2.58 to -2.17 and a significance level of 0.00. CONCLUSION: This study concludes that CPR is an effective screening tool and that it can be used to predict postnatal outcomes in patients with oligohydramnios. Clinical prediction rules were found to be a more effective screening tool, with a sensitivity of 92%, a specificity of 77.27%, and a diagnostic accuracy of 92.3%.
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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 | 0.002 | 0.012 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".