Developing classification criteria for discoid lupus erythematosus: an update from the World Congress of Dermatology 2015 meeting
Bibliographic record
Abstract
Currently, no standardized classification criteria exist for cutaneous lupus erythematosus. With increased interest in studying cutaneous lupus erythematosus, specifically discoid lupus erythematosus, it is our aim to apply previously adopted methods from rheumatology to dermatologic diseases to develop feasible, validated, and standardized classification criteria useful in both academic and community practice. Here we report the progress to date to define discoid lupus erythematosus using clinical, histopathologic, and serologic features by means of a Delphi method-using a series of iterative questionnaires sent to expert stakeholders. We present specific updates from the World Congress of Dermatology 2015 meeting, at which a nominal group of expert stakeholders met to discuss the results of round 1 of the Delphi process to further clarify and harmonize specific classification items for inclusion into round 2.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".