Current biomarkers in inclusion body myositis
Bibliographic record
Abstract
Inclusion body myositis (IBM) is an idiopathic muscle disorder primarily affecting adults above the age of 50. IBM is characterized by weakness in the knee extensor and deep finger flexor muscles due to muscle atrophy and fibroadipose replacement. Dynamometry and manual muscle testing are commonly used to assess patient muscle strength, while magnetic resonance imaging and electromyography studies identify the patterns of muscle atrophy and motor unit potentials. Although the underlying pathophysiological mechanisms of IBM are still unknown, common histopathological markers include rimmed vacuoles and inclusions. The immune system is also largely implicated in pathogenesis, as skeletal muscle in IBM overexpresses major histocompatibility complex I (MHC-I), and cluster of differentiation (CD) 8+ T-cells, and features endomysial inflammation. Antibodies to the cytosolic 5'-nucleotidase 1A (cN1A) protein have been associated with IBM but have low sensitivity and specificity. As many classic features of IBM present only in advanced stages of disease, there are substantial challenges to the diagnosis and monitoring of IBM progression in its early stages. Identifying early diagnostic biomarkers and new biomarker signatures associated with IBM disease progression is necessary for clinical trial readiness.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".