Visceral abdominal obesity – is there an increased prevalence in men presenting with testicular teratoma?
Bibliographic record
Abstract
BACKGROUND: There is evidence to suggest a link between the accumulation of visceral abdominal adipose tissue and an increased incidence of prostate, endometrial, breast, and colonic cancer. PURPOSE: To investigate whether an increase in ratio of visceral to subcutaneous abdominal adipose tissue is demonstrated in patients with testicular teratoma. MATERIAL AND METHODS: Following ethical approval, 22 male patients who had undergone staging computed tomography (CT) between 2004 and 2007 for testicular teratoma were identified from our database. Abdominal adipose tissue distribution for these 22 patients was compared with that of 22 control patients, standardized for age, sex, and body mass index. Visceral and subcutaneous adipose tissue volumes were calculated from a single axial CT slice at the level of the umbilicus. A two-sample t test for the difference in volume ratio between the two groups was used. A P value of < 0.05 was considered statistically significant. RESULTS: There was a statistically significant difference in the mean ratio of visceral to subcutaneous volumes between the teratoma patients and controls (P=0.02). The ratio in teratoma patients was 1.56 times greater than seen in control patients. CONCLUSION: Patients with testicular teratoma have a relatively greater proportion of abdominal visceral adipose tissue compared with controls. This is concordant with published literature for other malignancies.
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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".