Evaluation of Visceral Adiposity Index and Vitamin D Status in Colon Cancer: Is Visceral Obesity the Missing Link?
Bibliographic record
Abstract
Background: We aimed to evaluate the effect of visceral obesity and vitamin D status on colon cancer and to investigate the possible link between visceral obesity and vitamin D in those patients. Methods: This case-control study included 60 colon cancer patients and 40 subjects as control. Clinical, anthropometric, and pathological data were collected. Calculation of visceral adiposity index (VAI) and detection of vitamin D (25(OH)D) levels were performed and compared between groups. Results: There were significant differences in VAI and level of 25(OH)D between both groups. Moreover, we found a significantly higher prevalence of vitamin D deficiency in the patient’ group (53.3%) versus the control group (32.5%). There was a significant different mean of VAI in vitamin D deficient patients versus non-deficient patients (P = 0.024). We found a significantly different means of VAI and vitamin D in the patients’ group with different TNM stages, as higher stages are associated with a lower level of vitamin D and higher VAI. Conclusions: VAI and 25(OH)D were different in colon cancer patients compared with control. Likewise, they had different means with different TNM stages. Vitamin D may augment the inflammatory status in visceral obesity which is involved in tumorigenesis of colon cancer. J Endocrinol Metab. 2021;11(5):115-122 doi: https://doi.org/10.14740/jem762
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".