Near‐Infrared (NIR) Spectroscopy of Amniotic Fluid (AF) Distinguishes Between AGA and LGA Infants
Bibliographic record
Abstract
Background NIR of AF has been used to characterize fetal lung maturity and prematurity. However, this approach has been seldom used to classify infants according to birth weight for gestational age. Objectives We explored the possibility that NIR of 2nd trimester AF could identify early metabolomic differences between appropriate‐ (AGA, n=494) and large‐ (LGA, n=51)‐for‐gestational age infants. A secondary objective explored whether these AF profiles differed by maternal pre‐pregnancy BMI: normal BMI<24.9kg/m 2 (n=310) vs overweight/obese BMI>25 kg/m 2 (n=168). Methods Eight NIR functional groups (1600‐2400nm) were selected (CH, SH, POH, ROH, amide, amine, lactate and glucose); their values and calculated ratios were compared among experimental groups using Mann‐Whitney U‐test and Kruskal‐Wallis one‐way ANOVA test, followed by post‐hoc comparisons. Results Thirteen functional group ratios differed. LGA infants had higher AF glucose relative to CH, SH, POH and ROH and higher AF amines relative to POH, ROH, glucose and amides. In contrast, AGA infants had higher POH and ROH relative to glucose, amines and amides and higher CH:glucose and SH:glucose compared to AF of LGA infants. When subdivided by BMI categories, only AGA infants of overweight/obese mothers had higher AF glucose compared to normal weight mothers; surprisingly this maternal BMI‐based difference did not exist for LGA infants. Conclusion NIR spectral profiles of 2nd trimester AF differed between LGA and AGA infants but maternal BMI was not sufficient to fully explain early 2 nd trimester metabolic perturbations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".