Characteristics of magnetic resonance imaging biomarkers in a natural history study of golden retriever muscular dystrophy
Bibliographic record
Abstract
AbstractThe goal of this study was to assess whether magnetic resonance imaging (MRI) biomarkers can quantify disease progression in golden retriever muscular dystrophy (GRMD) via a natural history study. The proximal pelvic limbs of ten GRMD and eight normal dogs were scanned at 3, 6, and 9–12months of age. Several MRI imaging and texture analysis biomarkers were quantified in seven muscles. Almost all MRI biomarkers readily distinguished GRMD from control dogs; however, only selected biomarkers tracked with longitudinal disease progression. The biomarkers that performed best were full-length muscle volume and a texture analysis biomarker, termed heterogeneity index. The biceps femoris, semitendinosus and cranial sartorius muscles showed differential progression in GRMD versus control dogs. MRI features in GRMD dogs showed dynamic progression that was most pronounced over the 3- to 6-month period. Volumetric biomarkers and water map values correlated with histopathological features of necrosis/regeneration at 6-months. In conclusion, selected MRI biomarkers (volume and heterogeneity index) in particular muscles (biceps femoris, semitendinosus, and cranial sartorius) adjusted for age effect allow distinction of differential longitudinal progression in GRMD dogs. These biomarkers may be used as surrogate outcome measures in preclinical GRMD trials.
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 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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".