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Record W2915523397 · doi:10.3233/bmr-160561

Factors associated with non-specific low back pain in children aged 10–12 from Bucaramanga, Colombia: A cross-sectional study

2019· article· en· W2915523397 on OpenAlexaff
Adriana Angarita Fonseca, Milena Boneth-Collante, Claudia Lucía Ariza-García, Javier Parra-Patiño, Julián David Corredor-Vargas, Andrea Paola Villamizar-Niño

Bibliographic record

VenueJournal of Back and Musculoskeletal Rehabilitation · 2019
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCross-sectional studyMedicineEnvironmental healthPathology

Abstract

fetched live from OpenAlex

BACKGROUND: There are no Colombian studies published that assess non-specific low back pain (NSLBP) risk factors in children. OBJECTIVE: To determine the factors associated with NSLBP in 73 children (19.2% girls) aged 10-12 years in one military school in Bucaramanga, Colombia. METHODS: A questionnaire was used to obtain information of risk factors. Subsequently, children's weight and height were measured. The backpack was weighed at the beginning of each day from Monday to Friday. Crude and adjusted prevalence ratios were calculated, with their respective 95% confidence interval (CI). RESULTS: The one-month prevalence of NSLBP was 39.7% (95% CI 28.4-51.9). In the multivariate analysis, carrying backpacks wearing between 12% and 20% of body weight, having a perception that the backpack is very heavy, and being a passive smoker increase the likelihood of NSLBP, while being 11-year-old compared to 10-year-old decreases the likelihood of having NSLBP, adjusted for gender, body mass index, and history of LBP in parents. CONCLUSIONS: High prevalence of low back pain was found in children between 10 and 12 years old. The study of the decisive factors of low back pain is important to identify children at risk, as well as to develop efficient primary prevention programs.

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)0.003
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.360
Teacher spread0.332 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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