Clinical assessment of nutrition status score and body mass index in newborns
Bibliographic record
Abstract
Background: Fetal growth restriction can occur at any gestational period and is affected by maternal, placental and environmental factors. These factors can cause neonatal mortality or morbidity and long term sequalae. Thereby it is important to assess the nutritional status at birth. Clinical assessment of nutrition status score (CAN) assessment based on birth centile became important. Objectives: Aim was to compare CAN score and BMI birth centiles using pre-designated cut offs for assessing fetal nutrition. Materials and Methods: 1000 newborns cross-Sectional descriptive study. Department of Paediatrics, K.S. Hegde Medical Academy, Mangalore. Nov-2015 to June-2017. Inclusion criteria included term neonates with gestational age >37 completed weeks of gestation by dates or ultra-sonogram. Neonates with major congenital malformation or syndromes were excluded. Results: Out of 1000 newborns, 259 newborns (25.9%) had BMI below 10th centile out of which 99(9.9%) had BMI less than 3rd centile suggesting severe malnutrition. Malnutrition as per CAN Score was seen in 319(31.9%) infants. When CAN score and BMI was compared, among the 353 babies who had CAN score less than 25 indicating FM, only 129(36.5%) had BMI less than 10th centile. In 130 of the 259 infants with BMI less than 10th centile, CAN score was normal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".