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Record W2282977616 · doi:10.1016/j.jped.2015.11.003

Fat mass index performs best in monitoring management of obesity in prepubertal children

2016· article· en· W2282977616 on OpenAlexfundno aff
Luís Pereira‐da‐Silva, Mónica Pitta‐Grós Dias, Elisabete Dionísio, Daniel Virella, Marta Alves, Catarina Diamantino, Anabela Alonso, Gonçalo Cordeiro‐Ferreira

Bibliographic record

VenueJornal de Pediatria · 2016
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
FundersCanadian Nuclear Safety Commission
KeywordsMedicineBody mass indexObesityKappaCohortPediatricsFat massWeight lossInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: An early and accurate recognition of success in treating obesity may increase the compliance of obese children and their families to intervention programs. This observational, prospective study aimed to evaluate the ability and the time to detect a significant reduction of adiposity estimated by body mass index (BMI), percentage of fat mass (%FM), and fat mass index (FMI) during weight management in prepubertal obese children. METHODS: In a cohort of 60 prepubertal obese children aged 3-9 years included in an outpatient weight management program, BMI, %FM, and FMI were monitored monthly; the last two measurements were assessed using air displacement plethysmography. The outcome measures were the reduction of >5% of each indicator and the time to achieve it. RESULTS: The rate of detection of the outcome was 33.3% (95% CI: 25.9-41.6) using BMI, significantly lower (p<0.001) than either 63.3% using %FM (95% CI: 50.6-74.8) or 70.0% (95% CI: 57.5-80.1) using FMI. The median time to detect the outcome was 71 days using FMI, shorter than 88 days using %FM, and similar to 70 days using BMI. The agreement between the outcome detected by FMI and by %FM was high (kappa 0.701), but very low between the success detected by BMI and either FMI (kappa 0.231) or %FM (kappa 0.125). CONCLUSIONS: FMI achieved the best combination of ability and swiftness to identify reduction of adiposity during monitoring of weight management in prepubertal obese children.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.512

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)0.0000.000

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 head0.011
GPT teacher head0.262
Teacher spread0.252 · 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

Citations26
Published2016
Admission routes1
Has abstractyes

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