Department of Obstetrics and Gynecology
Bibliographic record
Abstract
Prediction of Preterm Birth Preterm birth is one of the major causes of neonatal death; thus, better diagnostics are necessary for the subsequent intensive care.However, it is still difficult to predict women who will develop preterm birth with high positive predictive value.Consequently, we have been investigating the possibility of maternal peripheral leukocytes as a marker to predict women with a risk of preterm labor using leukocyte migration assay, in collaboration with the University of Alberta, Canada. 2) Fetal Ischemic Brain InjuryFetal hypoxic ischemic encephalopathy is the leading cause of cerebral palsy, although the mechanism of cerebral palsy caused by fetal ischemia has not been elucidated yet.Collaborating with a group from the University of Gothenburg, Sweden, we are attempting to reveal the mechanism of ischemic cerebral palsy. 3) Novel Ultrasonographic TechniqueIt is important to diagnose the bleeding point on the uterine wall in patients with postpartum hemorrhage and to decide how to manage and treat it.We are developing a novel ultrasonographic imaging system using contrast media with the support of Hitachi Ltd.This new technique enables visualization of uterine bleeding at the bedside, which may be a more accurate and rapid modality for identification of the focus of intractable bleeding, leading to safer management of patients.As above, to improve maternal and fetal outcome during the perinatal and peripartum period, international joint research between industry and universities is currently being conducted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.256 | 0.070 |
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".