Comparison of therapeutic agents' short‐term effects on facial and scalp actinic keratosis: A network meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Care for actinic keratosis (AK) can be improved with more knowledge on the relative of effect of indicated therapies. OBJECTIVES: Using network meta-analyses, we quantitatively determined the comparative "short-term" effects of interventions in adults with facial and scalp AK. METHODS: On February 28, 2023, evidence from the peer-reviewed literature was systematically obtained from OVID, the Cochrane Central Register of Controlled Trials and ClinicalTrials.gov. We analyzed data from studies published in English, of a trial design, and investigating the effect of an actinic keratosis monotherapy. Patient complete clearance, patient partial clearance or lesion-specific clearance across adults were analyzed at 8-12 weeks after therapy. Patient complete clearance pertained to proportion of participants who experienced complete clearance of actinic keratosis lesions; patient partial clearance corresponded to percentage of subjects who achieved at least 75% clearance of actinic keratosis lesions; lesion-specific clearance represented the percentage of all lesions that were cleared. In the main (i.e., base) analyses, nodes were analyzed only at the level of the agent. RESULTS: Data from a total of 84 studies were used-across which 22 active agents were identified. Estimates of interventions' surface under the cumulative ranking curve rankings and (pairwise) relative effects were estimated. Across the three outcomes, fluorouracil 5% was ranked the most effective. CONCLUSIONS: Our work is the first to provide information on covariate-adjusted relative effects of actinic keratosis therapies- including the more recently reported treatments-for the face and scalp; this knowledge may help physicians and patients make more informed decisions.
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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 | 0.034 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.051 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".