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Record W2991155926 · doi:10.1016/j.pmedr.2019.101022

Questions on ‘Intervention effects of a kindergarten-based health promotion programme on obesity related behavioural outcomes and BMI percentiles’

2019· article· en· W2991155926 on OpenAlexaff
Colby J. Vorland, Andrew W. Brown, Chanaka Kahathuduwa, John Dawson, Nana Gletsu‐Miller, Theodore K. Kyle, Lehana Thabane, David B. Allison

Bibliographic record

VenuePreventive Medicine Reports · 2019
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsMcMaster UniversityImpact
FundersNational Institutes of HealthUniversity of OxfordLaura and John Arnold FoundationUniversity of AlabamaEgg Nutrition CenterUniversity of LouisvilleNortharvest Bean Growers AssociationUniversity of Alabama at BirminghamDairy ManagementGordon and Betty Moore FoundationTexas Tech UniversityCoca-Cola
KeywordsPercentileObesityHealth promotionIntervention (counseling)Promotion (chess)OverweightMedicinePsychologyGerontologyEnvironmental healthPublic healthPsychiatryPolitical scienceStatisticsNursingMathematics

Abstract

fetched live from OpenAlex

Kobel et al. (2019) report results of a cluster randomized trial examining the effectiveness of the “Join the Healthy Boat” kindergarten intervention on BMI percentile, physical activity, and several exploratory outcomes. The authors pre-registered their study (Steinacker et al., 2016) and described the outcomes and analysis plan in detail previously (Kobel et al., 2017), which are to be commended. However, we noted four issues that some of us recently outlined in a paper on childhood obesity interventions: 1) ignoring clustering in studies that randomize groups of children, 2) changing the outcomes, 3) emphasizing results that were statistically significant from a host of analyses, and 4) using self-reported outcomes that are part of the intervention (Brown et al., 2019).

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)0.001
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 score0.010
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.021
GPT teacher head0.318
Teacher spread0.297 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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