Comparison of segmental and global bioimpedance spectroscopy errors using generalizability theory
Bibliographic record
Abstract
The generalizability theory, an expansion of classic true-score reliability theory, was used to investigate the generalizability of observed segmental extracellular fluid (ECF) and intracellular fluid (ICF) distribution measurements. The test instrument was a Xitron Hydra ECF/ICF bioimpedance analysis system model 4200, Xitron Technologies, San Diego, CA. Fifty American healthy men (17-72 years) and 50 American healthy women (17-76 years) volunteered as participants. Xitron continuous segmental ECF-ICF procedures for testing leg segmental data were followed for testing participants in both the standing erect and lying supine postures. A two-facet, person-by-trial, completely crossed design was used. All facets were treated as random. During a one-day session each subject was tested involving 20 trials for the standing erect posture and 20 trials for the lying supine posture. Data on each fluid measurement, each body posture and each sex group were independently analysed. The analyses revealed that the trial factor accounted for less than 0.2% of the total variance for ECF and ICF scores. ECF and ICF generalizability coefficients for the segmental method were 0.99 or greater. In comparing ECF segmental to ECF global, results showed generalizability coefficients were similar. However, ICF segmental coefficients were larger than the coefficients produced by the global method. In conclusion, the segmental method appeared more reliable than the global method, under the conditions of this study.
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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 | 0.037 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".