Bibliographic record
Abstract
1. W. Douglas Biggar, MD* 1. *Professor of Paediatrics, University of Toronto, Bloorview Macmillan Children’s Centre, Toronto, Ontario, Canada After completing this article, readers should be able to: 1. Describe the pathogenesis of Duchenne muscular dystropy (DMD). 2. Describe the natural history and late complications of DMD. 3. List the laboratory investigations available to diagnose muscle disorders. 4. Discuss the management for DMD. MD was born following a normal pregnancy and delivery. His parents were nonconsanguineous, and the family history was unremarkable. He had a 6-year-old brother who was well. MD walked when he was 18 months old, 6 months later than his brother. He was a toe-walker and had large calves. He never ran as well as his brother, and he could not hop on one foot. By 4 years of age, he had difficulty climbing stairs at home and the ladder at the neighborhood playground. His pelvic girdle muscles were weak, he walked with a rocking, side-to-side, waddling gait (Trendelenburg), and he developed lumbar lordosis. He fell more frequently for no apparent reason. His parents became concerned and sought medical advice. Laboratory testing revealed a serum creatine kinase value 50 times greater than normal. On genetic testing, Duchenne muscular dystrophy (DMD) was diagnosed. His weakness progressed. To get up after falling, he would have to use his hands to climb up his legs to stand. In the first grade, his academic performance was judged to be delayed. His teachers noticed that his concentration was poor; he had difficulty staying on task. He scored higher on his verbal intelligence quotient than on his performance intelligence quotient. He also was teased by other children at recess. His parents elected to have him repeat the first grade. He required an educational assistant for classroom activities. He also displayed some obsessive-compulsive behaviors. When he was 10 years old, walking became more difficult, and he required a wheelchair for ambulation. His weight gain became excessive as …
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".