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Excessive body weight in developmental coordination disorder: A systematic review and meta-analysis

2024· review· en· W4400413017 on OpenAlexaboutno aff
Leyre Gambra, Samuele Cortese, Pablo Lizoain, Diana Rodriguez Romero, Úrsula Paiva, Carmen Gándara, Gonzalo Arrondo, Sara Magallón

Bibliographic record

VenueNeuroscience & Biobehavioral Reviews · 2024
Typereview
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
FundersEuropean Social FundAgencia Estatal de InvestigaciónDepartment of Health and Social CareResearch Executive AgencyNational Institute for Health and Care ResearchUniversidad Internacional de La Rioja
KeywordsMeta-analysisOverweightObesityPsychologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

Evidence on the link between developmental coordination disorder (DCD) and obesity and overweight is mixed. Based on a pre-registered protocol (PROSPERO: CRD42023429432), we conducted the first systematic review/meta-analysis on the association between DCD and excessive weight. Web of Science, PubMed and an institutional database aggregator were searched until the 18th of December 2023. We assessed study quality using the Newcastle-Ottawa Scale and study heterogeneity using Q and I2 statistics. Data from 22 studies were combined, comprising 11,330 individuals out of which 1861 had DCD. The main analysis showed a significant association between DCD and higher body weight (OR:1.87, 95 % CI =1.43, 2.44). Meta-regression analyses indicated that the relationship was mediated by age, with stronger effects in studies with higher mean age (p 0.004). We conclude that DCD is associated with obesity and overweight, and this association increases with age. Our study could help to implement targeted prevention and intervention measures.

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)0.015
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.032
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0190.029
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.148
GPT teacher head0.413
Teacher spread0.265 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations7
Published2024
Admission routes1
Has abstractyes

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