From Neonatal to Fetal Neurology: Some Clues for Interpreting Fetal Findings
Bibliographic record
Abstract
Abstract As early as possible, neonatologists try to identify neonates at risk of unfavorable neurodevelopmental outcomes. They are fairly reliable in predicting very poor outcomes as well as optimal outcomes. However, within these two extremes, the prediction still remains a challenge. Immaturity of the neonatal brain constitutes a limit in itself. During decades with the growing knowledge of brain development, many methods have been developed for neurological assessment of the neonate. Neither of them applied alone was perfect in terms of clinical applicability, sensitivity, reproducibility and specificity. The motor function is the first to provide the clinician with clues. Higher functions, in particular language and other cognitive functions, will develop later. However, recent researchers give credit to the brainstem for controlling exceedingly rudimentary learning-related cognitive-like activity. At present, the anticipation of late emerging developmental disabilities remains difficult even though early motor dysfunction has repeatedly been associated with a higher risk of intellectual or other learning disabilities. Despite our modest recent contribution to the domain of prediction, further studies on welldefined high risk populations with rigorous methodology that aim to demonstrate these links are still needed. Besides neurological observations, research is in process of including behavioral and stress/ reactivity measures; feasibility and benefits have to be demonstrated. At present, fetal neurology is supported by neonatal neurology. Obstetricians are wise enough to take from both methods described above the elements they are able to transpose to fetal life. A comparative table of neonatal and fetal assessment is to be found elsewhere. As for neonatal neurology, the future of fetal neurology will have to rely on short- and long-term follow-up studies to define the predictive value of the chosen items. Obstetricians will have to be as patient as pediatricians, to work, step by step, towards defining optimality and impairment. They will have to be very careful when deciding to interrupt pregnancies; at the time being, such decisions are restricted to cases of very severe impairment. In line with the spectrum described above, they can expect to find more cases with moderate to mild abnormalities than cases with severe ones. However the most pleasant aspect for the echographer is to check fetal optimality. Just as a newborn infant categorized as at risk of brain damage is competent enough to demonstrate CNS integrity from birth, a high risk fetus will soon be competent enough to demonstrate CNS integrity before birth.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".