Reliability of Head, Neck, and Trunk Anthropometric Measurements Used for Predicting Segment Tissue Masses in Living Humans
Bibliographic record
Abstract
Soft and rigid tissue mass prediction equations have been previously developed and validated for the segments of the upper and lower extremities in living humans using simple anthropometric measurements. The reliability of these measurements has been found to be good to excellent for all measurement types (segment lengths, circumferences, breadths, skinfolds). However, the reliability of the measurements needed to develop corresponding equations for the head, neck, and trunk has yet to be determined. The purpose of this study was to quantify the inter- and intrameasurer reliability of 34 surface anthropometric measurements of the head, neck, and trunk segments. Measurements (11 lengths, 7 circumferences, 11 breadths, 5 skinfolds) were taken twice separately on 50 healthy, university-age individuals using standard anthropometric tools. The mean inter- and intrameasurer measurement differences were fairly small overall, with 64.7% and 67.6% of the relative differences less than 5%, respectively. All measurements, except for the right lateral trunk, had intraclass correlation coefficients (ICCs) greater than 0.75, and coefficients of variation (CVs) less than 10%, indicating good reliability overall. These results are consistent with previous work for the extremities and provide support for the use of the defined surface measurements for future tissue mass prediction equation development.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".