Structured Expert Consensus on Actinic Keratosis: Treatment Algorithm Focusing on Daylight PDT
Bibliographic record
Abstract
BACKGROUND: A practical and up-to-date consensus among experts is paramount to further improve patient care in actinic keratosis (AK). OBJECTIVES: To develop a structured consensus statement on the diagnosis, classification, and practical management of AK based on up-to-date information. METHODS: A systematic review of AK clinical guidelines was conducted. This informed the preparation of a 3-round Delphi procedure followed by a consensus meeting, which combined the opinions of 16 clinical experts from 13 countries, to construct a structured consensus statement and a treatment algorithm positioning daylight photodynamic therapy (dl-PDT) among other AK treatment options. RESULTS: The systematic review found deficiencies in current guidelines with respect to new AK treatments such as ingenol mebutate and dl-PDT. The Delphi panel established consensus statements across definition, diagnosis, classification, and management of AK. While the diagnosis of AK essentially rests on the nature of lesions, treatment decisions are based on several clinical and nonclinical patient factors and diverse environmental attributes. Participants agreed on ranked treatment preferences for the management of AK and on classifying AK in 3 clinical situations: isolated AK lesions requiring lesion-directed treatment, multiple lesions within a small field, and multiple lesions within a large field, both requiring specific treatment approaches. Different AK treatment options were discussed for each clinical situation. CONCLUSIONS: The results provide practical recommendations for the treatment of AK, which are readily transferable to clinical practice, and incorporate the physician's clinical judgement. The structured consensus statement positioned dl-PDT as a valuable option for patients with multiple AKs in small or large fields.
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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 | 0.272 | 0.314 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".