A practical guide on the use of imiquimod cream to treat lentigo maligna
Bibliographic record
Abstract
Lentigo maligna (LM) is a common in situ melanoma subtype arising on chronically sun-damaged skin and mostly affects the head and neck region. Localisation in cosmetically sensitive areas, difficulty to obtain wide resection margins and advanced patient age/comorbidities have encouraged investigation of less invasive therapeutic strategies than surgery in managing complex cases of LM. Radiotherapy and imiquimod have emerged as alternative treatment options in this context. The treatment of LM with imiquimod cream can be challenging due to the nature of the disease including its often large size, variegated appearance, involvement of adnexal structures, poorly defined peripheral edge and frequent localisation close to sensitive structures such as the eyes and lips, and elderly patients with multiple comorbidities. Prolonged and unpredictable inflammatory reaction and side effects and compliance with a patient-delivered therapy can also be challenging. In the literature to date, studies evaluating the use of imiquimod to treat LM have utilised varying methodologies and provided short follow-up and these limitations have impaired the development of clear guidelines for dosage and management of side effects. Based on our multidisciplinary experience and review of the literature, we propose practical clinical strategies for the use of imiquimod for treating LM, detailing optimal administration procedures in various clinical scenarios and long-term management, with the aim of facilitating optimal patient 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.016 |
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".