PM112 Association of Cardiac Cachexia and Heart Failure Types: Right and Left (With Reduced or Preserved Ejection Fraction)
Bibliographic record
Abstract
The aim of this study was to determine, from the methodologic standpoint, the effect of the presence or absence of intravenous contrast on body composition variables obtained by analysis of computed tomography (CT) images.Triphasic abdominal (noncontrast, arterial phase, and portovenous phase contrast) CT scans from 111 patients were analyzed by two independent assessors at the third lumbar vertebral level using SliceOmatic software (version 5.0, TomoVision, Montreal, Canada). Variables included skeletal muscle index (SMI), fat and fat-free mass (FM and FFM, respectively), and mean skeletal muscle Hounsfield units (SMHU).Mean SMHU was lowest in the noncontrast phase (29.4, standard deviation [SD] 8.9 HU), followed by arterial (32.4, SD 9.3 HU) then portovenous phases (34.9, SD 9.4 HU). The mean skeletal muscle attenuation was significantly different depending on the phase of the scan in which the images were obtained. Calculated FM was significantly lower in both arterial (28.6, SD 8.8 kg, P < 0.0001) and portovenous phase scans (28.5, SD 8.9 kg, P < 0.0001) when compared with noncontrast (29.2, SD 8.9 kg). The mean FFM was not significantly different as measured on noncontrast, arterial, or portovenous phase CT scans (48, SD 11.2; 48.1, SD 9.8; and 48.6, SD 10.2 kg, respectively). No difference was seen in SMI. Interobserver reliability was high.The definition of myosteatosis should include a standardized phase of CT for analysis and this should be incorporated within its definition. However, as the magnitudes of the differences were relatively small, the effect of the phase of the scan on predicting outcome needs to be determined.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".