DP01 Survey on the management of dysplastic naevus
Bibliographic record
Abstract
Abstract Although dysplastic naevus (DN) is a frequent diagnosis, there is significant controversy on its definition and gradation and its relationship to melanoma risk. Therefore, there is no consensus over optimal management, and surveys from Australia, Canada and the USA have shown variation in clinical practice. Dysplastic naevus with involved histological margins has not been shown to progress to melanoma. To assess UK management of DN depending on the grade of atypia and reported excision margin, we circulated a questionnaire via email to dermatologists in Scotland, north Wales, Liverpool and Manchester. Seventy-two responded, of whom 46 (64%) were consultants. The cohort was generally highly experienced, with 10 (14%) responders with 1–5 years’ dermatology experience, 15 (21%) with 6–10 years, 27 (37%) with 11–20 years and 20 (28%) with > 20 years’ experience. Management of DN varied greatly. For mild DN excised with < 1-mm margin, 59 (82%) agreed that no further management was required. Opinions were divided when margins were involved in mild DN, with 14 (19%) agreeing that no further management was required, 42 (58%) choosing to re-excise and six (8%) to re-excising only any residual pigmentation. Most dermatologists reassured patients with moderately DN with margins < 1 mm (49, 68%); 18 (25%) preferred to re-excise. For moderate DN with involved margins, six respondents (8%) felt that no further management was necessary and 54 (75%) would re-excise. For severe DN with margins > 1 mm, 16 (22%) would still re-excise and six (8%) would agree management in the multidisciplinary team meeting (MDT). For severe DN with margins < 1 mm, 41 (57%) would re-excise and seven (10%) would agree management in the MDT. Lastly, for severe DN with involved margins, 62 (86%) would re-excise and seven (10%) would agree management in the MDT. This study has shown comparable results with those from Australia and the USA, whereby most dermatologists would re-excise moderate and severe DN with involved margins. Significant numbers indicated that they would re-excise in the case of close histological margins: this is unnecessary, and more standardized management may reduce expenditure. Limitations of the study include the usual potential sources of bias in a questionnaire survey; it should be noted that reported behaviour may differ from actual practice. However, this is the first UK study on this common clinical scenario. The move to report DN as low- or high-grade may help to reduce variation, but our survey suggests that some patients are currently undergoing unnecessary procedures.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".