Lipoatrophy in Canadian Multiple Sclerosis Patients Receiving Glatiramer Acetate
Bibliographic record
Abstract
Glatiramer acetate (GA) is indicated for use in patients with relapsing-remitting multiple sclerosis (RRMS). One of the side effects of GA is lipoatrophy, a localized loss of subcutaneous fat around the injection site. A Canadian postmarketing observational study on MS patients receiving GA was designed to provide further insight on lipoatrophy and to assess the utility of using digital images of lipoatrophic lesions to confirm diagnosis and define severity. From a Teva Canada Innovation–sponsored MS support program, with a total population size at the time of the study of 5770 patients receiving GA, patients with self-reported lipoatrophy were identified by a nurse who took photographs of possible lipoatrophic lesions and recorded patient demographic and clinical data during a home visit. Photographs were then assessed by an immunodermatologist (AAG) to confirm lipoatrophy and grade severity. The patients with self-reported lipoatrophy who consented to participate in the study (N = 206) had a mean age of 47.2 years; 98.1% were female, and 54.4% had been taking GA for more than 5 years. Clinical images from 192 patients were available for review. Lipoatrophy was confirmed in 85% of patients. Lesions occurred at all injection sites with equal frequency, with no significant differences in severity. This study demonstrates that most GA-treated patients with lipoat-rophy can correctly diagnose the condition and that digital photographs can play a role in confirming the presence of lipoatrophy, allowing nurses to initiate timely and appropriate interventions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".