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Record W2105975285 · doi:10.1093/ije/dyr108

Commentary: Measuring nutritional status of children

2011· letter· en· W2105975285 on OpenAlexaff
Daniel J. Corsi, Malavika A. Subramanyam, S. V. Subramanian

Bibliographic record

VenueInternational Journal of Epidemiology · 2011
Typeletter
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsEnvironmental healthMedicineGerontology

Abstract

fetched live from OpenAlex

Leg length has been suggested as a proxy for nutritional and environmental exposures in childhood given the associations observed in some Western populations.1,2 Sanjay Kinra et al.3 present a careful assessment of this hypothesis in an Indian population in this issue of the International Journal of Epidemiology and observe no association between nutritional supplementation and relative leg length, and relative lower leg length, among adolescents in the Hyderabad cohort. Although intriguing, given previous findings4,5 and the proposed sensitivity of ‘lower’ leg length as a marker for nutritional status,6 the null finding reported by Kinra and colleagues is in accord with other studies in non-Western populations.7,8 To our knowledge, only one other study has specifically examined the association of nutritional supplementation in early childhood with leg length in ‘childhood’.4 This study set in 1930s Britain investigated the effects of a year-long nutritional supplementation programme on change in components of height after 1 year. The children in the Hyderabad study, in contrast, had the potential to receive the supplementation for about 6 years. Another difference is that the children in the British study were about 9 years old at follow-up whereas the Indian study focused on 13–18 year olds. Importantly, whereas the supplements were directly given to the children or their families in the British study, the intervention in the Hyderabad trial was at the village level: a child was considered to be in the treatment group if s/he resided in a village where the supplementation programme was being implemented. Kinra et al.3 treat their study as a quasi-experimental cluster trial and use the Intention-to-Treat (ITT) principle in their analysis. This approach is a standard practice but, by discarding data from eligible children because of missing baseline data,9 the investigators did not entirely adhere to principles of ITT. The study might have been strengthened by clarifying whether statistical methods recommended specifically to handle missing data (individual and cluster) for ITT in cluster randomized trials were applied.10

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)0.007
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.070
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.045
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.002
Science and technology studies0.0050.003
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0700.047
Insufficient payload (model declined to judge)0.0060.005

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 head0.076
GPT teacher head0.339
Teacher spread0.262 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations35
Published2011
Admission routes1
Has abstractno

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