Relationship between Ponderal Index, Mid-Arm Circumference/ Head Circumference Ratio and Clinical Assessment of Nutritional Status Score (CANSCORE) in the Determination of Nutritional Status of Newborn at Birth in Nigeria
Bibliographic record
Abstract
Background: Early and accurate assessment of the nutritional status of newborns is important to many clinicians because of the potential immediate and late sequelae of malnutrition. Objective: To assess the relationship between different methods of assessing the nutritional status of neonates. Methods: Subjects were consecutive, live, singleton, full term neonates delivered in the hospital. The birth weights, Ponderal index, Mid arm circumference/head circumference ratio, birth weight for gestational age using intrauterine growth charts and Clinical Assessment of Fetal Nutritional Status Score (CANSCORE) were used to determine the nutritional status in the first 24 hours of life. Results: Of 386 subjects, 172 (44.6%) were males and 214 (55.4%) females. Nutritional status assessment using various indices showed the following prevalence of malnutrition: using birth weights, 54 (14.0%) were LBW; MAC/HC ratio showed 56 (14.5%), with PI, 64(16.6%), weight for gestational age,112(29.0%) were SGA and CANSCORE showed 90(23.3%) as malnourished among the babies.MAC/HC showed a better specificity and had a more positive correlation than PI when compared to CANSCORE whilst PI showed a better sensitivity than MAC/HC when evaluated against CANSCORE. Conclusions: Prevalence of FM is high in this study. Intrauterine growth charts and CANSCORE appeared to identify more babies with FM than other methods. CANSCORE in this study has revealed the rising trend in the prevalence of FM when compared with other studies with similar methodology. Early routine assessment of the nutritional status of newborns should be carried out so as to reduce the risk of increased morbidity and mortality associated with fetal malnutrition.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".