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Record W4312130020 · doi:10.1186/s13104-022-06259-x

Comparative study of stunting measurement in children using WHO procedure and Growth Length Mat in Ghana

2022· article· en· W4312130020 on OpenAlexaff
Nafisatu Bukari, Alhassan Danaa, Abdulai Mubarak, Wilfred W. Forfoe, Ayishetu Gariba, Zakari Ali

Bibliographic record

VenueBMC Research Notes · 2022
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsMcGill University
FundersWorld Health Organization
KeywordsInterpretabilityMedicineGold standard (test)Environmental healthUnder-fivePediatricsDemographyComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: Height data is not useful immediately without further processing. The Growth Length Mat (Stunting Mat) was designed to stunting directly. However, the Mat has not been validated for use in Ghana. This study compared stunting measured with the Mat and stunting measured following the WHO recommended procedure. We sampled 163 children aged 6-24 months in the Bono region of Ghana. We also explored the acceptability and interpretability of the two procedures among mothers and healthcare givers. RESULTS: The prevalence of stunting was 3.7% and 11.7% using the Mat and WHO procedures respectively. The Growth Length Mat had very low sensitivity (21.0%) but high specificity (98.6%) of detecting stunting in children. However, in younger aged children, the Mat was more accurate in detecting stunting. Both healthcare workers and caregivers found it easy to interpret the stunting status of children using the Mat. We conclude that the Growth Length Mat was less sensitive at detecting child stunting compared to the current gold standard of measuring stunting in younger children. There are possibilities to improve the accuracy and utility of the Mat for measuring stunting in low-resource settings by re-designing the mat to be more age appropriate.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.012
Threshold uncertainty score0.671

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.243
GPT teacher head0.418
Teacher spread0.175 · 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 teacher head, 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

Citations1
Published2022
Admission routes1
Has abstractyes

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