<scp>OP</scp>22.04: Low pulmonary blood flow demonstrated by Doppler and <scp>MRI</scp> in late onset <scp>IUGR</scp>
Bibliographic record
Abstract
Objectives: In spite of the high dose administration of artificial lung surfactant, some neonates show the severe respiratory disorder (SRD) in premature delivery.In these cases, the problem is not the deficiency of surfactant, but the maturity of the lung.Since lung fluid secreted by fetal lungs is essential for the fetal lung maturity, the measurement of water content of the lung on T2-weighted images may predict the fetal lung maturity.Therefore, we measured the fetal lung-to-liver signal intensity ratio (LLSIR) on T2-weighted images and examined the relations between LLSIR and presence of the SRD after birth.The purpose of this study is to determine the fetal LLSIR on T2-weighted images as an accurate prenatal evaluating method for fetal lung maturity.Methods: One hundred twenty fetuses who underwent MRI examination in various indications after 22nd week of gestation participated in this study with their parents' consent.LLSIR was measured on T2-weighted images of MRI.We examined the changes of the ratio with the progress of gestational week at first and then the relations between LLSIR and presence of the SRD after birth.The best cutoff value of the LLSIR to predict respiratory outcome after birth was calculated using Receiver Operating Characteristic (ROC) analysis.Results: LLSIR correlated significantly with advancing of gestational age.The relationship between LLSIR(y) and gestational age(x) was shown as y = 0.037x + 0.97 (R = 0.31, p < 0.005).The non-SRD group had higher LLSIR when compared with the SRD group (2.16 ± 0.30 vs. 1.53 ± 0.40, p < 0.001).ROC curve analysis showed that fetuses with an LLSIR below 2.00 were more likely to develop SRD (sensitivity: 100%, specificity: 75%). Conclusions:The fetal LLSIR on T2-weighted images is an important and useful marker to diagnose the fetal lung maturity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".