Actinic Keratoses (AK): An Exploratory Questionnaire-Based Study of Patients’ Illness Perceptions
Bibliographic record
Abstract
Background: Decreased illness perception among actinic keratoses (AK) patients is a major barrier to the effective management of AK. Objective: We aimed to investigate patients’ illness and treatment perceptions, their correlation to demographics and AK/skin cancer history, and secondarily the influence of these perspectives on treatment and sunscreen use. Materials and Methods: Participants completed questionnaires based on the Brief Illness Perception Questionnaire and statistical analysis was performed. Results: In total, 208 AK patients were enrolled. A large proportion were poorly aware of the disease (41.4%), with less than half (43%) being familiar with AK. Patients were aware of the chronic nature of the disease and its correlation to sunlight regardless of demographic characteristics. The level of education played a role in disease awareness (p = 0.006), and treatment plan perception (p = 0.002). The increase in sunscreen protection after AK diagnosis was higher in women (p = 0.009) and younger patients (p = 0.044). Patients’ concerns regarding treatment were mainly related to the duration (30%) and effectivity (25%). Dermatologists’ statements highlighting that AK are precancerous lesions (86.2%) influenced patients’ willingness for treatment. Conclusion: Improved awareness of AK is necessary to increase treatment seeking and compliance, regarding both treatment and sunscreen use. Dermatologists’ statements may have critical influence on patients’ decisions to receive 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".