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Validity of a simplified screening instrument for assessing overweight children in a dental setting: a cross sectional study

2017· other· en· W6977217467 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2017
Typeother
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsnot available
Fundersnot available
KeywordsOverweightCross-sectional studyBody mass indexObesityPublic healthHealth care

Abstract

fetched live from OpenAlex

Abstract Background Obesity, with its rising prevalence among Canadians and its estimated cost of $2 billion annually in Canada, is no longer considered a cosmetic issue, but a major health issue that imposes a great burden on the healthcare system and economy. This cross sectional study aims to evaluate the feasibility of identifying the weight status of 6 to 11 year-old children in a university dental clinic using a simplified overweight screening instrument. Methods One hundred sixty eight healthy children were enrolled. Weight and height were measured and overweight/obesity status was assessed using two techniques: 1) the 2007 World Health Organization Body Mass Index (BMI)-for-age reference Tables, 2) simplified overweight screening instrument without BMI calculation. Measures of overall, positive, and negative percent agreement between the two approaches were computed. Results The children’s average weight, height, BMI and BMI z-score were respectively 32.6 ± 9.5 kg, 133.8 ± 10.7 cm, 17.8 ± 3.2, and 0.4 ± 1.0. The overall, positive, and negative percent agreement between the two screening approaches were respectively, 89%, 100%, and 83%. Conclusion This study demonstrated the feasibility and parental acceptance of weight, height and BMI measurement in a dental setting and evidence that supports the validity of a new simplified approach to assess children’s weight status without having to compute BMI. Trial registration NCT02637752 . Registered 18 December 2015.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.775
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.000

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 head0.061
GPT teacher head0.328
Teacher spread0.268 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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