Developing juvenile localized scleroderma (jLS) consensus treatment regimens for comparative effectiveness studies
Bibliographic record
Abstract
A core group of pediatric rheumatologists based in CARRA was formed to evaluate and develop standardized jLS regimens and assessments. Two dermatologists who study jLS and a lay person were also recruited. Members reviewed literature on current treatments and assessments, and through surveys and Delphi processes, developed criteria to define different levels of disease severity, generated consensus regimens for jLS treatment, and agreed upon clinical parameters to evaluate disease activity and damage. Preliminary regimens and assessments were discussed with the CARRA membership, and modified based on feedback. We have developed criteria to define high, moderate, and low disease severity, and standardized clinical activity and damage assessments for jLS. An atlas of lesion images was generated to facilitate scoring the level of several parameters (erythema, hyperpigmentation, hypopigmentation, dermal atrophy, and subcutaneous tissue loss). See table 1 . Through use of the Delphi process, the jLS core group was able to generate consensus initial treatment regimens for MTX, MTX with oral CS, and MTX with intravenous CS, including a general tapering regimen for oral corticosteroids. Table 2 . There is a need for standardized jLS disease assessments and treatment regimens to be able to compare treatment efficacy. A CARRA subgroup has developed consensus assessments and treatment regimens for jLS. The efficacy of these regimens will be evaluated in future comparative effectiveness studies.
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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 | 0.524 | 0.410 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".