Predictive model for immunotherapy of alopecia areata with diphencyprone.
Bibliographic record
Abstract
BACKGROUND: Immunotherapy with diphencyprone (diphenylcyclopropenone) is used in the treatment of alopecia areata (AA). Response rates have varied in the literature. OBJECTIVES: To determine the efficacy of diphencyprone therapy for AA in the largest reported cohort of patients; to identify patient and treatment factors predictive of therapeutic success; and to develop a practical model for predicting patient response. METHODS: The medical records of 148 consecutive patients treated with diphencyprone were reviewed. A clinically significant response to diphencyprone therapy was defined as a cosmetically acceptable response or greater than 75% terminal hair regrowth. Survival analyses using the Kaplan-Meier method and the Cox proportional hazards model were performed to determine significant factors predictive of regrowth and relapse. RESULTS: Using a survival analysis model, the cumulative patient response at 32 months was 77.9% (95% confidence interval, 56.8%-98.9%). Variables independently associated with clinically significant regrowth were age at onset of disease and baseline extent of AA. Older age at onset of AA portended a better prognosis. A cosmetically acceptable end point was obtained in 17.4% of patients with alopecia totalis/universalis, 60.3% with 75% to 99% AA, 88.1% with 50% to 74% AA, and 100% with 25% to 49% AA. A lag of 3 months was present between initiation of therapy and development of significant hair regrowth in the first responders. Relapse after achieving significant regrowth developed in 62.6% of patients. CONCLUSIONS: Response to diphencyprone treatment in AA is affected by baseline extent of AA and age at disease onset. A prolonged treatment course might be necessary. A predictive model has been developed to assist with patient prognostication and counseling.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".