Body Mass Index, Age, Sexual Maturation and Incidence of Hyperlordosis in Children and Adolescents
Bibliographic record
Abstract
Abstract Introduction: Hyperlordosis can cause several degenerative spinal pathologies in children and adolescents. Objective: Determine whether body mass index, age and sexual maturation predict the occurrence of hyperlordosis in children and adolescents. Method: The study analyzed 380 students aged between 10 and 18 years. Body mass index was evaluated using the reference values suggested by the Fitnessgram test battery, and sexual maturation through Tanner’s scale of self-assessed pubic hair growth. Postural assessment was conducted using the DIPA photogrammetry method, version 3.1. (Digital Image Based Postural Assessment) The SPSS 24.0 program was used to analyze the data, and the following statistical tests were applied: chi-squared, Mann-Whitney, Fisher’s exact and binary logistic regression. Results: There was statistical significance between hyperlordosis, girls’ age and puberty in boys (p <0.05). The adjusted binary logistic regression for the girls (OR = 0.656) indicated that this disorder declined by 34% with advancing age and for stage P2 of puberty in boys (OR = 4.292), and the likelihood of boys’ presenting with hyperlordosis at this stage is approximately 4-fold higher. There was no statistical significance between body mass index and hyperlordosis (p> 0.05). Conclusion: The girls’ age and boys’ stage of puberty were associated with the occurrence of hyperlordosis.
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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 | 0.000 | 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) | 0.003 | 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".