Sensitivities, Specificities, and Positive Predictive Values ofSimple Indices of Body Fat Distribution
Bibliographic record
Abstract
Centralized obesity has been associated with increased risk of non-insulin dependent diabetes and cardiovascular disease. Paramount to a sensitive index of body fat distribution is that it contain a measure of lower limb fat (Ashwell et al. 1978; 1982; Mueller and Stallones 1981). However, many epidemiological studies of body fat distribution, which have used skinfold measurements, have been limited to estimating centralized obesity from the triceps and subscapular or other conventional upper body sites. The purpose of this study was to evaluate the sensitivities, specificities, and positive predictive values of skinfold indices of body fat distribution when only sites on the upper body are available. We were able to do this in a large population-based data set, the Canadian YMCA-LIFE study, which included adults 25 to 64 years of age and skinfold sites from upper and lower anatomical regions of the body.Sensitivities, specificities, and positive predictive values did not vary systematically with age group, sex or obesity level. Sensitivities (mean = 70%) and positive predictive rates (mean = 65%) were moderate for the most common two site index (triceps/triceps + subscapular) and were not notably improved with the addition of the suprailiac site. Simple percent extremity fat indices (e.g. triceps/(triceps + subscapular) X 100) were as effective in discriminating body fat distribution groups as an index involving the same variables in the form of a vector of log transformed measurements. Substituting lower limb fat (medial calf) for arm fat (triceps) in simple percent indices, provided important additional information (mean sensitivity = 77%, mean positive predictive rate = 70%).
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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".