Consistency of Lower-Body Dimensions Using Surface Landmarks and Simple Measurement Tools
Bibliographic record
Abstract
Caia, J, Weiss, LW, Chiu, LZF, Schilling, BK, and Paquette, MR. Consistency of lower-body dimensions using surface landmarks and simple measurement tools. J Strength Cond Res 30(9): 2600-2608, 2016-Body dimensions may influence various types of physical performance. This study was designed to establish the reliability and precision of bilateral lower-body dimensions using surface anatomic landmarks and either sliding calipers or goniometry. Fifty university students (25 men and 25 women) were measured on 2 separate occasions separated by 48 or 72 hours. A small digital caliper was used to acquire longitudinal dimensions of the feet, whereas a larger broad-blade caliper was used to measure lower-limb, hip, and pelvic dimensions. Quadriceps angle (Q-angle) was determined through surface goniometry. Data for all foot and lower-limb dimensions were both reliable and precise (intraclass correlation coefficient (ICC) ≥0.72, SEM 0.1-0.5 cm). Measures of Q-angle were also reliable and precise (ICC ≥0.85, SEM 0.2-0.4°). Findings from this investigation demonstrate that lower-body dimensions may be reliably and precisely measured through simple practical tests, when surface anatomic landmarks and standardized procedures are used. Although intertester reliability remains to be established, meticulous adherence to specific measurement protocols is likely to yield viable output for lower-body dimensions when more sophisticated methods are unavailable or inappropriate.
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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 | 0.011 | 0.033 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".