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Record W3160040026 · doi:10.1038/s41598-021-89595-5

Spinal pain increases the risk of becoming overweight in Danish schoolchildren

2021· article· en· W3160040026 on OpenAlexaff
Lise Hestbæk, Ellen Aartun, Pierre Côté, Jan Hartvigsen

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsCentre for Disability Prevention and Rehabilitation
FundersTrygFondenSyddansk UniversitetKræftens Bekæmpelse
KeywordsDanishOverweightMedicineObesityInternal medicine

Abstract

fetched live from OpenAlex

Spinal pain is common in adolescence, and overweight in children and adolescence is an increasing public health problem globally. Since musculoskeletal pain is a known barrier for physical activity which potentially can lead to overweight, the primary objective of this study was to determine if self-reported lifetime spinal pain in 2010 was associated with being overweight or obese in 2012 in a cohort of 1080 normal-weighted Danish children, aged 11-13 years at baseline. Overweight was based on body mass index measured by trained staff. Spinal pain was self-reported by questionnaires during school hours. Estimates were adjusted for relevant covariates. The 2-year incidence rate of overweight was 5.3% (95% CI 3.98-7.58) for children with spinal pain at baseline versus 1.6% (95% CI 0.19-5.45) for children without. There was stepwise and statistically significant increased risk of overweight with increasing frequency of pain and for having pain in more than one part of the spine. Despite the short follow-up time where only 40 children developed overweight, these results indicate that spinal pain might increase the risk of subsequent overweight.

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)0.009
metaresearch head score (Gemma)0.002
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 score0.024
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.389
Teacher spread0.351 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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