Approach to the diagnosis of congenital myopathies
Bibliographic record
Abstract
Over the past decade there have been major advances in defining the genetic basis of the majority of congenital myopathy subtypes. However the relationship between each congenital myopathy, defined on histological grounds, and the genetic cause is complex. Many of the congenital myopathies are due to mutations in more than one gene, and mutations in the same gene can cause different muscle pathologies. The International Standard of Care Committee for Congenital Myopathies performed a literature review and consulted a group of experts in the field to develop a summary of (1) the key features common to all forms of congenital myopathy and (2) the specific features that help to discriminate between the different genetic subtypes. The consensus statement was refined by two rounds of on-line survey, and a three-day workshop. This consensus statement provides guidelines to the physician assessing the infant or child with hypotonia and weakness. We summarise the clinical features that are most suggestive of a congenital myopathy, the major differential diagnoses and the features on clinical examination, investigations, muscle pathology and muscle imaging that are suggestive of a specific genetic diagnosis to assist in prioritisation of genetic testing of known genes. As next generation sequencing becomes increasingly used as a diagnostic tool in clinical practise, these guidelines will assist in determining which sequence variations are likely to be pathogenic.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".