A Study to Determine the Frequency Rate of Scoliosis Disorder and Compare the Anthropometric Characteristics of Normal versus the Scoliosis Diagnosed Students
Bibliographic record
Abstract
INTRODUCTION: Postural deformities are commonly acquired disorders that occur throughout the life. The purpose of this research was to determine and compare the frequency of scoliosis disorder and anthropometric characteristics of normal versus the disordered Students. MATERIALS AND METHODS: This was a cross-sectional study that was performed on 1416 girls and boys of elementary school students in the city of Kashan in education year 2010-2011. Adams bending test was employed to examine 1416 students to identify the disorder. Seca scale was employed to measure weight and inflexible tape was used to measure the height of students. SPSS software was employed to analyze the data. RESULTS: The result of analysis showed that 63.8 percent of students were boys and 36.2 percent were girls. The frequency of scoliosis in boys and girls was 29.8 and 24.2 percent, respectively. Independent t-test result showed that there was a significant difference between the height and weight of normal versus the scoliosis identified boys and girls student (P=0.004, 0.031; 0.0001, 0.041). CONCLUSION: These types of studies are conducted regularly to identify poor postural cases at an early stage. The identification of acquired deformities at an early stage is important since it provides the opportunity to take the appropriate measures to correct them. Early identification of scoliosis is vital to maximize effectiveness of treatment.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".