Expert Recommendations on Facilitating Personalized Approaches to Long-term Management of Actinic Keratosis: The Personalizing Actinic Keratosis Treatment (PAKT) Project
Bibliographic record
Abstract
Actinic keratoses are pre-malignant skin lesions that require personalized care, a lack of which may result in poor treatment adherence and suboptimal outcomes. Current guidance on personalizing care is limited, notably in terms of tailoring treatment to individual patient priorities and goals and supporting shared decision-making between healthcare professionals and patients. The aim of the Personalizing Actinic Keratosis Treatment panel, comprised of 12 dermatologists, was to identify current unmet needs in care and, using a modified Delphi approach, develop recommendations to support personalized, long-term management of actinic keratoses lesions. Panellists generated recommendations by voting on consensus statements. Voting was blinded and consensus was defined as ≥ 75% voting 'agree' or 'strongly agree'. Statements that reached consensus were used to develop a clinical tool, of which, the goal was to improve understanding of disease chronicity, and the need for long-term, repeated treatment cycles. The tool highlights key decision stages across the patient journey and captures the panellist's ratings of treatment options for attributes prioritized by patients. The expert recommendations and the clinical tool can be used to facilitate patient-centric management of actinic keratoses in daily practice, encompassing patient priorities and goals to set realistic treatment expectations and improve care outcomes.
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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 | 0.086 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".