Network meta-analysis of the outcome ‘participant complete clearance’ in nonimmunosuppressed participants of eight interventions for actinic keratosis: a follow-up on a Cochrane review
Bibliographic record
Abstract
The conclusions of pairwise meta-analyses of interventions for actinic keratosis (AK) are limited due to the lack of direct comparison between some interventions. Consequently, we performed a network meta-analysis for eight treatments [5-aminolaevulinic acid (ALA)-photodynamic therapy (PDT), cryotherapy, diclofenac 3% in 2·5% hyaluronic acid (DCF/HA), 5-fluorouracil (5-FU) 0·5% or 5·0%, imiquimod (IMI) 5%, ingenol mebutate (IMB) 0·015-0·05%, methyl aminolaevulinate (MAL)-PDT and placebo/vehicle (including placebo-PDT)] to determine their relative efficacies. As part of a prior Cochrane systematic review, different databases and grey literature were searched for randomized controlled trials up to April 2012. The inclusion criteria were parallel-group studies with nonimmunosuppressed participants: (i) reporting 'participant complete clearance' and (ii) comparing at least two of the interventions. Thirty-two publications met the criteria and they included the following number of individual or pooled studies (n) and total number of participants (N) for the different interventions: 5-FU 0·5% (n = 4, N = 169), 5-FU 5·0% (n = 2, N = 44), ALA-PDT (n = 6, N = 739), cryotherapy (n = 2, N = 174), DCF/HA (n = 5, N = 299), IMI (n = 14, N = 1411), IMB (n = 3, N = 560), MAL-PDT (n = 7, N = 557) and placebo (n = 32, N = 2520). Network analyses using a random-effects Bayesian model were carried out with the software ADDIS v1.16.1. The interventions were ranked as follows based on calculated probabilities and odd ratios: 5-FU > ALA-PDT ≈ IMI ≈ IMB ≈ MAL-PDT > cryotherapy > DCF/HA > placebo. This efficacy ranking was obtained based on the current available data on 'participant complete clearance' from randomized controlled trials and the analysis model used. However, several other factors should also be considered when prescribing a treatment for AK.
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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 | 0.030 | 0.056 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.057 |
| 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 | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".