Impact of dermoscopy and short-term sequential digital dermoscopy imaging for the management of pigmented lesions in primary care: a sequential intervention trial
Bibliographic record
Abstract
BACKGROUND: Studies have shown the benign to malignant ratio of excised pigmented skin lesions is suboptimal in primary care. OBJECTIVES: To assess the impact of dermoscopy and short-term sequential digital dermoscopy imaging (SDDI) on the management of suspicious pigmented skin lesions by primary care physicians. METHODS: A total of 63 primary care physicians were trained in the use of dermoscopy and SDDI (interventions) and then recruited pigmented lesions requiring biopsy or referral in routine care by naked eye examination. They were then given a dermatoscope and the option of a SDDI instrument, and change of diagnosis and management was assessed. RESULTS: Following the use of the interventions on 374 lesions a total of 163 lesions (43.6%) were excised or referred, representing a reduction of 56.4%. Of the 323 lesions confirmed to be benign, 118 (36.5%) were excised or referred, leading to a reduction of 63.5% (P < 0.0005) in those requiring excision or referral. The baseline naked eye examination benign to melanoma ratio was 9.5 : 1 which decreased to 3.5 : 1 after the diagnostic interventions (P < 0.0005). Of the 42 malignant lesions included in the study (34 melanoma, six pigmented basal cell carcinoma and two Bowen disease) only one in situ melanoma was incorrectly managed (patient to return if changes occur) resulting in the correct management of 97.6% and 97.1% of malignant pigmented lesions and melanoma, respectively. CONCLUSIONS: In a primary care setting the combination of dermoscopy and short-term SDDI reduces the excision or referral of benign pigmented lesions by more than half while nearly doubling the sensitivity for the diagnosis of melanoma.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".