Impact of body mass index on the intensity of pain and quality of life in an obese population
Bibliographic record
Abstract
There is a growing incidence of obesity around the world. According to the World Health Organization (WHO), obesity is conferred by a body mass index (BMI) greater than 30 kg m-2. Several studies demonstrated that individuals experience pain with the rise in the body mass index. The causal link between the two remains unclear as yet. World Health Organization defines the quality of life (QoL) as, "an individual's perception of their position in life, in context of the culture and value systems in which they live, and with their expectations, standards and concerns". Quality of life is a sizeable multidimensional term that typically involves subjective evaluations of both the positive and the negative aspects of life. To study the effect of body mass index on the intensity of pain and quality of life. One hundred four obese individuals with the presence of pain were provided with the McGill Pain Questionnaire, Short Form Health Survey-36. Their BMI was taken. The type of study was cross-sectional, and the study design was a survey (Questionnaire) method. BMI and pain intensity are directly related to each other. Obesity leads to an increase in the pain intensity and affects the quality of life of obese individuals. As the BMI increases the pain intensity of the individuals also increases. The quality of life depends upon BMI. The quality of life is affected in the obese population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".