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Evaluation of Nutritional Status of Rural Bengalee Primary School Boys (6-9 Years) in Comparison to Indian Children

2013· article· en· W2127772087 on OpenAlexvenueno aff
Subrata Dutta, Prakash Chandra Dhara

Bibliographic record

VenueInternational Journal of Child Health and Nutrition · 2013
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEnvironmental healthPediatricsDemography

Abstract

fetched live from OpenAlex

A cross sectional study was undertaken to determine nutritional status and growth pattern of 410 rural primary school boys (6-9 years of age) belong from lower socioeconomic status according Kuppuswamy's socioeconomic scale (2012) in West Midnapore District of West Bengal. The daily nutritional intake of the children was measured by weighing raw and cooked foods and also by 24 hrs recall method. The body mass indexes (BMI), body composition, protein/calory adequacy status, protein-energy ratio of the primary school boys were measured to assess the nutritional status. The food and nutrient intake of the subjects were compared with their respective Indian values (NNMB, 2002). The diet of rural school boys was found to be imbalanced with plenty intake of milk and lower in intake of cereals, pulses but higher in intake of vegetables than that of their Indian counterpart. It was observed that the diets are predominantly more deficient in calories than protein. Most of the boys had normal body weight and only a little number of boys was overweight or underweight and the protein energy ratio is lower than ICMR recommended value except the age group of 7 years and the maximum percentage (about 20%) of underweight boys is found in the age group of 9 years. It was further observed that there was a significant correlation between body composition and BMI (p<0.001).

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.020
GPT teacher head0.340
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2013
Admission routes1
Has abstractyes

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Same venueInternational Journal of Child Health and NutritionSame topicChild Nutrition and Water AccessFrench-language works237,207