Noninvasive measurements of body composition and body water via quantitative magnetic resonance, deuterium water, and dual-energy x-ray absorptiometry in awake and sedated dogs
Bibliographic record
Abstract
OBJECTIVE: To compare quantitative magnetic resonance (QMR), dual-energy x-ray absorptiometry (DXA), and deuterium oxide (D2O) methods for measurement of total body water (TBW), lean body mass (LBM), and fat mass (FM) in healthy dogs and to assess QMR accuracy. ANIMALS: 58 Beagles (9 months to 11.5 years old). PROCEDURES: QMR scans were performed on awake dogs. A D2O tracer was administered (100 mg/kg, PO) immediately before dogs were sedated, which was followed by a second QMR or DXA scan. Jugular blood samples were collected before and 120 minutes after D2O administration. RESULTS: TBW, LBM, and FM determined via QMR were not significantly different between awake or sedated dogs, and means differed by only 2.0%, 2.2%, and 4.3%, respectively. Compared with results for D2O dilution, QMR significantly underestimated TBW (10.2%), LBM (13.4%), and FM (15.4%). Similarly, DXA underestimated LBM (7.3%) and FM (8.4%). A significant relationship was detected between FM measured via D2O dilution and QMR (r(2) > 0.89) or DXA (r(2) > 0.88). Even though means of TBW and LBM differed significantly between D2O dilution and QMR or DXA, values were highly related (r(2) > 0.92). CONCLUSIONS AND CLINICAL RELEVANCE: QMR was useful for determining body composition in dogs and can be used to safely and rapidly acquire accurate data without the need for sedation or anesthesia. These benefits can facilitate frequent scans, particularly in geriatric, extremely young, or ill pets. Compared with the D2O dilution method, QMR correction equations provided accurate assessment over a range of body compositions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".