Neck Circumference as a Screening Tool for Metabolic Syndrome among Lebanese College Students
Bibliographic record
Abstract
Metabolic syndrome (MetS) is a cluster of symptoms that, when present, increase the risk for cardiovascular disease. There is a need for reliable screening tools that are ethnically sensitive. Two hundred and sixty-six college students were assessed anthropometrically. They had a fasting blood sample drawn, and blood pressure measured. They then completed a demographic questionnaire and The International Physical Activity Questionnaire (IPAQ). The prevalence of MetS was found to be 10.1% in males and 4.5% in females. Neck circumference (NC) was positively associated with BMI in males (r = 0.55, p < 0.001) and females (r = 0.53, p < 0.001) and was positively associated with hip circumference in both males (r = 0.47, p < 0.001) and females (r = 0.50, p < 0.001) and with waist circumference in males (r = 0.46, p < 0.001) and females (r = 0.49, p < 0.001.) An area under the curve (AUC) was calculated using receiver operating characteristics (ROC), and NC > 38 cm in males and NC> 36 cm in females were found to be appropriate cut-offs for diagnosing MetS. NC is a reliable and non-invasive screening tool that can be used to screen for MetS in males. NC could also serve as an anthropometric instrument to assess abdominal obesity and could be valuable for college students.
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 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".