Detecting Low Fat-free Mass Index From Anthropometric Z-scores In Female College Athletes
Bibliographic record
Abstract
Body composition assessment is often included in the athlete’s program. Detecting low levels of some components, like the fat-free mass (FFM), may help identify female athletes at risk of developing hormonal problems from training- or diet-related issues. In this regard, a fat-free mass index (FFMI) below 16 kg/m2 has been suggested to identify a low FFMI in women. Some laboratory methods, like DXA, can assess FFM with relatively acceptable precision; however, those methods are impractical in field settings. Conversely, anthropometry is an accessible method and can be used to estimate “Phantom” z-scores to assess body size. However, whether the Phantom-derived z-scores can be used to identify a low FFMI is still to be determined. PURPOSE: To determine whether the Phantom-derived z-scores could be used for detecting a low FFMI in female college athletes. METHODS: The sample comprised 58 women participating in different sports at the national competitive level. We took anthropometric measurements according to the ISAK protocol. Similarly, the body composition was analyzed with a whole-body DXA scan to get the FFMI. We calculated the Phantom z-scores for the arm, thigh, and calf corrected circumferences (corrected for the triceps, anterior thigh, and medial calf skinfold, respectively) and the uncorrected forearm circumference. Next, we calculated the arithmetic mean from these four z-scores (ZC) to predict the DXA-derived FFMI and determine the ZC equivalent to a FFMI of 16 kg/m2 using linear regression. Data are presented as mean ± SD or median [25th, 75th percentile]. RESULTS: The descriptives were the following: age 21.2 ± 2.1 y, weight 60.9 ± 8.7 kg, height 164.0 ± 7.0 cm, FFMI 16.2 ± 1.4 kg/m2, and ZC 0.34 [-0.23, 0.77]. The ZC was a statistically significant predictor of FFMI (FFMI = 15.8 + ZC * 1.3) (r = 0.79, SEE = 0.86, p < 0.001). The ZC cut-off point was 0.1; however, after considering one SEE, the equivalent lower and higher cut-off points were -0.5 and 0.8 ZC CONCLUSIONS: The Phantom-derived z-scores help set cut-off points to identify low FFMI in female college athletes, serving as a screening tool in field conditions. We propose that the desirable ZC should be >0.8, and a ZC < -0.5 would imply a higher probability of low FFMI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".