Research on full-term brain metabolites in predicting long-term neurological development in preterm infants
Bibliographic record
Abstract
Objective To explore the value of detecting brain metabolites of preterm infants at full term for predicting the neurodevelopmental level, and to provide the basis for early clinical intervention. Methods Thirty cases of preterm infants were collected from the Neonatal Intensive Care Unit and Neuro-Rehabilitation Department of Guangzhou Women and Children′s Medical Center between May 2015 and March 2016, then they were checked by adopting brain magnetic resonance imaging and magnetic resonance spectroscopy at corrected full term, and assessed by using Alberta Infant Motor Scale(AIMS) and Gesell developmental scale evaluation at corrected age of 6 months and corrected of age 1 year old. Results In the 30 cases of preterm infants, 19 cases were male, 11 cases were female, and the gestational age was 27+ 3-31 weeks, and average gestational age was (28.8±1.0) weeks, and the birth weight was 800-1 400 g[(1 176.3±145.1) g]. The study found that myo-inositol (MI), MI/creatine (Cr) in basal ganglia were negatively correlated with the development quotient at corrected age of 1 year old(r=-0.465, -0.532; all P 0.05). Conclusions Preterm infants brain metabolites at full term contribute to predicting neurodevelopmental level.MI, Lac, MI/Cr, Lac/Cr are of values for predicting neurodevelopmental level, and MI/Cr is the best predictor.Among frontal lobe, basal ganglia, hippocampus, periventricular and cerebellum, the periventricular is the best area for predicting neurodevelopmental level.Corrected age of 1 year old maybe the best time to predicting neurodevelopmental level. Key words: Infant, preterm; Magnetic resonance spectroscopy; Neurodevelopment
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".