Dysplastic naevi: To shave, or not to shave? A retrospective study of the use of the shave biopsy technique in the initial management of dysplastic naevi
Bibliographic record
Abstract
The management of dysplastic naevi is a controversial subject. This study sought to assess the usefulness of the shave biopsy technique in the initial management of dysplastic naevi, and to demonstrate the advantages over the punch biopsy technique. We report a retrospective observational study of histopathology specimens examined in one histopathology practice over a 14-month period. Patients who had a clinical diagnosis of 'dysplastic naevus', which had initially been biopsied using either a shave or punch biopsy, and then followed up with a full-thickness elliptical excision, were included in the study. Histopathological concordance between the shave and punch biopsy specimens and their respective follow-up elliptical excisions was compared. We found that 21 of 22 (95.5%) shave biopsies were concordant with their respective excision specimens, and that 29 of 41 (70.7%) punch biopsies were concordant with their respective elliptical excision specimens. Of the shave biopsy specimens reviewed, 66% showed that the dysplastic naevi were completely excised with the initial biopsy, compared with 21.2% of the punch biopsy specimens. These findings confirm that shave biopsies provide accurate diagnostic information in the assessment of dysplastic naevi. Shave biopsies enable the entire lesion to be submitted for histopathological assessment, improving the chances of an accurate diagnosis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".