The Accuracy of 2.5 mm Punch Biopsy in theDiagnosis of Skin Lesions
Bibliographic record
Abstract
Background: Punch biopsies are gaining widespread attention among medical professionals for their broad use and ease of access. Despite decades-long use, there needs to be more robust statistical evidence regarding their diagnostic accuracy. This study aims to evaluate the accuracy of the 2.5 mm punch biopsy in diagnosing skin lesions by comparing the histopathology of punch biopsies with that of excisional biopsies. Methods: In this retrospective study, a review of 4,000 charts, including skin lesions seen by a single plastic surgeon from 2016–2023, was conducted to identify patients who underwent a 2.5 mm punch biopsy of a lesion followed by a subsequent excisional biopsy. 206 charts were identified. Concord ance between punch and excisional histopathologies was used to calculate the efficacy of the 2.5 mm punch biopsy as a diagnostic tool. Results: Of 206, 141 skin lesions were characterized as cancerous by punch biopsy, all confirmed on subsequent excisional biopsy. 51 were deemed benign/precancerous on punch biopsy and confirmed by excision. 12 were identified as cancerous by punch biopsy but later characterized as benign/precancerous by excision. 2 were characterized as precancerous by punch biopsy and cancerous by excision. Analysis revealed that the 2.5 mm punch biopsy had a sensitivity of 98.6% (95% CI: 95.04%–99.83%) and specificity of 80.95% (69%–89.75%) in diagnosing skin lesions, which is statistically significant by several measures. Conclusion: The 2.5 mm punch biopsy is an accurate tool for diagnosing skin lesions. It can be applied to various anatomical sites and lesion sizes. Its non-suturing requirement may enhance cosmetic outcomes and ease-of-use.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".