Noninvasive measurements of body composition and body water via quantitative magnetic resonance, deuterium water, and dual-energy x-ray absorptiometry in cats
Bibliographic record
Abstract
OBJECTIVE: To compare quantitative magnetic resonance (QMR), dual-energy x-ray absorptiometry (DXA), and deuterium oxide (D2O) dilution methods for measurement of total body water (TBW), lean body mass (LBM), and fat mass (FM) in healthy cats and to assess QMR precision and accuracy. ANIMALS: Domestic shorthair cats (58 and 32 cats for trials 1 and 2, respectively). PROCEDURES: QMR scans of awake cats performed with 2 units were followed by administration of D2O tracer (100 mg/kg, PO). Cats then were anesthetized, which was followed by QMR and DXA scans. Jugular blood samples were collected before and 120 minutes after D2O administration. RESULTS: QMR precision was similar between units (coefficient of variation < 2.9% for all measures). Fat mass, LBM, and TBW were similar for awake or sedated cats and differed by 4.0%, 3.4%, and 3.9%, respectively, depending on the unit. The QMR minimally underestimated TBW (1.4%) and LBM (4.4%) but significantly underestimated FM (29%), whereas DXA significantly underestimated LBM (9.2%) and quantitatively underestimated FM (9.3%). A significant relationship with D2O measurement was detected for all QMR (r(2) > 0.84) and DXA (r(2) > 0.84) measurements. CONCLUSIONS AND CLINICAL RELEVANCE: QMR was useful for determining body composition in cats; precision was improved over DXA. Quantitative magnetic resonance can be used to safely and rapidly acquire data without the need for anesthesia, facilitating frequent monitoring of weight changes in geriatric, extremely young, or ill pets. Compared with the D2O dilution method, QMR correction equations provided accurate data over a range of body compositions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".