Computerized planimetry to assess clinical responsiveness in a phase II randomized trial of topical R333 for discoid lupus erythematosus
Bibliographic record
Abstract
Discoid lupus erythematosus (DLE) is a skin condition that causes red scaly lesions which often leave behind dyspigmentation (discolouration) and scars. It is an autoimmune disease, which means that the body's immune system attacks normal skin. Sunlight has also been shown to play a role in this disease with most patches, called lesions, occurring in body areas exposed to ultraviolet radiation. DLE involves the skin, with or without the type of lupus called systemic lupus erythematosus (SLE). About 15 to 30 percent of SLE patients will develop DLE. Janus Kinase, or JAK, are a group of enzymes in the body involved in the development of the disease. Researchers have been working to find medicines that will inhibit JAK as it is thought that doing so will improve patients’ symptoms. This study, from the United States and Canada, assessed the effectiveness of a therapy called R333. R333 is a ‘topical’ ointment, meaning a medicine applied directly to the skin, that works by blocking the signaling of JAK and spleen tyrosine kinase (Syk), which is also involved in the disease. In the trial, we compared R333 to a placebo ointment. The results showed that four weeks of R333 treatment did not result in significant clinical improvement. However, further studies with different formulations are necessary to determine the utility of JAK/Syk inhibitors in the treatment of autoimmune skin disease. In addition, it is often difficult to measure improvement of individual skin lesions when treated with topical medicines, when clearance may not be the same across all lesions. Therefore, we used the results of this study to evaluate an approach to assessing individual lesions. This method, called computerized planimetry, was used to digitally trace photos of specific skin lesions and was reliable for measuring the area of a DLE lesion. Therefore, this tool has the potential to be used to measure the extent of disease activity in individual lesions treated with topical medicines in future DLE trials.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".