Bibliographic record
Abstract
Objective: To integrate evidence and assess the risk factors associated with actinic keratosis (AK). Methods: Unrestricted searches were conducted on five electronic databases, with an end-date parameter of September 2021. We summarized the study characteristics and pooled the results from individual studies by using a random-effects model. The risk of bias was estimated using the Cochrane Risk of Bias Tool, and the quality of evidence was estimated according to the Newcastle–Ottawa Scale. Results: Sixteen studies were included in final analysis, and we assessed the AK risk among a variety of risk factors. Overall, the male sex (odds ratio (OR): 2.51; 95% confidence interval (CI): 1.94–3.25; P < 0.01), age >45 years (OR = 7.65, 95% CI: 2.95–19.86; P < 0.01), light Fitzpatrick skin phototype (OR = 2.32, 95% CI: 1.74–3.10; P < 0.01), light hair color (OR = 2.17, 95% CI: 1.40–3.36; P < 0.01), light eye color (OR = 1.67, 95% CI: 1.03–2.70; P = 0.04), freckles on face/arms (OR = 1.88, 95% CI: 1.37–2.58; P < 0.01), suffered positive history of other types of non-melanoma skin cancer (OR = 4.46, 95% CI: 2.71–7.33; P < 0.01), sunburns in childhood (OR = 2.33, 95% CI: 1.47–3.70; P < 0.01) and adulthood (OR = 1.50, 95% CI: 1.12-2.00; P < 0.01), severe sunburn (OR = 1.94, 95% CI: 1.62–2.31; P < 0.01), and chronic occupational and/or recreational sun exposure (OR = 3.22, 95% CI: 2.16–4.81; P < 0.01) increased the risk of AK. Moreover, sunscreen use (OR = 0.51, 95% CI: 0.34–0.77; P < 0.01) and history of atopy reduced the risk of AK. Sensitivity analysis yielded consistent results. The included studies showed a high risk of bias. Conclusion: We confirm several well-known AK risk factors and their quantitative data, and summarized the uncommon risk factors and protective factors. Our results may inform on the design and implementation of AK screening and educational programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".