Precision and reliability of strength (Jamar vs. Biodex handgrip) and body composition (dual-energy X-ray absorptiometry vs. bioimpedance analysis) measurements in advanced cancer patients
Bibliographic record
Abstract
Important deteriorations in body composition and strength occur and need to be accurately measured in advanced cancer patients (ACPs). The aim of this study was to establish the relationship between a single-frequency bioimpedance analyzer (BIA) and the dual-energy X-ray absorptiometer (DXA), as well as the Jamar handgrip dynometer and the Biodex handgrip attachment, and to determine the precision of each of these instruments in ACPs. Eighty-one ACPs with non-small-cell lung cancer and gastrointestinal cancer were recruited from the McGill University Health Centre (Montreal, Que.). Consecutive paired measurements, with repositioning between measurements, were obtained for total-body DXA, BIA, Biodex handgrip, and BIA plus Jamar handgrip. The total-body percent coefficient of variation (%CV) for the BIA and DXA were 1.34 and 1.56 for fat mass (FM), respectively, and 0.42 and 0.72 for fat free mass (FFM), respectively. The %CV for the Jamar and Biodex handgrips were 6.3 and 16.7, respectively. Bland-Altman plots were used to characterize the limits of agreement between DXA and BIA for FM (4.60 +/- 7.80 (-3.19 to 12.39) kg) and FFM (-1.87 +/- 7.16 (-9.03 to 5.29) kg). Both DXA and BIA demonstrate good short-term precision in ACPs. However, given its poor accuracy, it remains to be determined if BIA can be used to monitor ACPs for changes in total-body tissue composition as a function of time, whether for observation or response to treatment. Furthermore, because of wide limits of agreement, the DXA and BIA cannot be used interchangeably in research or clinical settings. The Jamar handgrip dynamometer shows more consistency than the Biodex handgrip attachment in ACPs, and should therefore be the preferred measure of changes in strength over time.
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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 | 0.008 | 0.025 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".