Correlation of signal attenuation–based quantitative magnetic resonance imaging with quantitative computed tomographic measurements of subchondral bone mineral density in metacarpophalangeal joints of horses
Bibliographic record
Abstract
OBJECTIVE: To evaluate the ability of signal attenuation-based quantitative magnetic resonance imaging (QMRI) to estimate subchondral bone mineral density (BMD) as assessed via quantitative computed tomography (QCT) in osteoarthritic joints of horses. SAMPLE POPULATION: 20 metacarpophalangeal joints from 10 horse cadavers. PROCEDURES: Magnetic resonance (MR) images (dorsal and transverse T1-weighted gradient recalled echo [GRE] and dorsal T2*-weighted GRE fast imaging employing steady-state acquisition [T2*-FIESTA]) and transverse single-slice computed tomographic (CT) images of the joints were acquired. Magnetic resonance signal intensity (SI) and CT attenuation were quantified in 6 regions of interest (ROIs) in the subchondral bone of third metacarpal condyles. Separate ROIs were established in the air close to the joint and used to generate corrected ratios and SIs. Computed tomographic attenuation was corrected by use of a calibration phantom to obtain a K(2)HPO(4)-equivalent density of bone. Correlations between QMRI performed with different MR imaging sequences and QCT measurements were evaluated. The intraobserver repeatability of ROI measurements was tested for each modality. RESULTS: Measurement repeatability was excellent for QCT (R(2) = 98.3%) and QMRI (R(2) = 98.8%). Transverse (R(2) = 77%) or dorsal (R(2) = 77%) T1-weighted GRE and QCT BMD measurements were negatively correlated, as were dorsal T2*-FIESTA and QCT (R(2) = 80%) measurements. Decreased bone SI during MR imaging linearly reflected increased BMD. CONCLUSIONS AND CLINICAL RELEVANCE: Results of this ex vivo study suggested that signal attenuation-based QMRI was a reliable, clinically applicable method for indirect estimation of subchondral BMD in osteoarthritic metacarpophalangeal joints of horses.
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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 | 0.002 | 0.012 |
| 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 | 0.001 | 0.000 |
| 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".