Statement from the frontal fibrosing alopecia international expert alliance: <scp>SOFFIA</scp> 2024
Bibliographic record
Abstract
BACKGROUND: As the incidence of frontal fibrosing alopecia (FFA) continues to rise, there is a need for an optimal treatment algorithm for FFA. OBJECTIVES: To produce an international consensus statement on the treatment modalities and prognostic indicators of FFA. METHODS: Sixty-nine hair experts from six continents were invited to participate in a three-round Delphi process. The final stage was held as a virtual meeting facilitated via Zoom. The consensus threshold was set at ≥66%. RESULTS: Of 365 questions, expert consensus was achieved in 204 (56%) questions following completion of the three rounds. Three additional questions were included at the final meeting. The category with the strongest consensus agreement was disease monitoring (9; 100%). Questions pertaining to physical therapies achieved the least category consensus (15; 40%), followed by systemic therapy (45; 43%). LIMITATIONS: The study lacked sufficient representation from Africa and South America. CONCLUSIONS: SOFFIA highlights areas of agreement and disagreement among experts. Robust research is warranted to provide evidence-based treatment recommendations.
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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 | 0.033 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".