Bibliographic record
Abstract
BACKGROUND: Assessment of primitive reflexes is one of the earliest, simplest, and most frequently used assessment tools among health care providers for newborns and young infants. However, very few data exist for high-risk infants in this topic. Among the various primitive reflexes, this study was undertaken particularly to describe the sucking, Babinski and Moro reflexes in high-risk newborns and to explore their relationships with clinical variables. METHODS: This study is a cross-sectional descriptive study. Sixty seven high-risk newborns including full-term infants required intensive care as well as premature infants were recruited in a neonatal intensive care unit using convenient sampling method. The sucking, Babinski and Moro reflexes were assessed and classified by normal, abnormal and absence. To explore their relationships with clinical variables, birth-related variables, brain sonogram results, and behavioral state (the Anderson Behavioral State Scale, ABSS) and mental status (the Infant Coma Scale, ICS) were assessed. RESULTS: The sucking reflex presented a normal response most frequently (63.5%), followed by Babinski reflex (58.7%) and Moro reflex (42.9%). Newborns who presented normal sucking and Babinski reflex responses were more likely to have older gestational age, heavier birth and current weight, higher Apgar scores, shorter length of hospitalization, better respiratory conditions, and better mental status assessed by ICS, but not with Moro reflex. CONCLUSIONS: High risk newborns presented more frequent abnormal and absence responses of primitive reflex and the proportions of the responses varied by reflex. Further researches are necessary in exploring diverse aspects of primitive reflexes and revealing their clinical implication in the high-risk newborns that are unique and different to normal healthy newborns. KEYWORDS: Primitive reflex; High risk infants; Korean; Moro reflex; Sucking reflex; Babinski reflex; The Anderson Behavioral State Scale; Infant Coma Scale.
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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 | 0.002 |
| 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".