Factors associated with low levels of lumbar strength in adolescents in Southern Brazil
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence and factors associated with low levels of lumbar strength in adolescents.METHOD: This was a cross-sectional study involving 601 adolescents, aged 14 to 17 years, enrolled in public schools in the western region of Santa Catarina State - Southern Brazil. Lumbar strength was analyzed by the lumbar extension test developed by the Canadian Society of Exercise Physiology, which proposes different cutoffs for boys and girls. Independent variables were sex, age, socioeconomic status, dietary habits, alcohol consumption, physical activity, and aerobic fitness. For data analysis, univariate and multivariate logistic regression were used, with significance level of 5%.RESULTS: The prevalence of low levels of lumbar strength was 27.3%. The population subgroups most likely to present low levels of lumbar strength were females (OR: 1.54, 95% CI : 1.06 to 2.23), adolescents with low levels of aerobic fitness (OR: 2.10, 95% CI: 1.41 to 3.11) and the overweight (OR: 2.28, 95% CI: 1.35 to 3.81).CONCLUSION: Almost one-third of the studied students have low levels of lumbar strength. Interventions in the school population should be taken with special attention to female adolescents, those with low levels of aerobic fitness, and those with overweight, as these population subgroups were most likely to demostrate low levels of lumbar strength.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".