Prevalence and onychoscopy features of benign longitudinal melanonychia in richly pigmented skin individuals
Bibliographic record
Abstract
Background Benign longitudinal melanonychia (BLM) is not readily documented in richly pigmented skin individuals. Objective To determine the population-based prevalence of BLM and to document its onychoscopy and clinical features. Patients and methods This was a cross-sectional study of 1304 participants from October to December 2022 at the Lagos State University Teaching Hospital, Lagos, following ethical clearance. The finger and toe nails of the participants were examined for BLM. Onychoscopy was conducted on individuals who had BLM. Data were analyzed using SPSS version 25. Simple means and frequencies are presented. Results The prevalence of BLM was 30%, occurred in 50.6% of females and was more prevalent in individuals aged greater than 60 years. The colour of BLM was brown in 80.6%, regular width, uniform colouration and spacing in 100%. BLM mostly occurred on one digit and occurred more on the finger nails than the toe nails. The thumb and the index fingers of the hand, the big toe and the second toe nails were predominantly affected. Conclusion Benign longitudinal melanonychia has a high prevalence in richly pigmented skin individuals and it occurs more in the seventh decade of life. It affects females more and affects the finger nails more than the toe nails. A family history, onset after trauma or pregnancy are uncommon. Onychoscopy reveals a predominantly brown colouration, a width of less than 3 mm, regular lines, uniform colouration, regular spacing and a regular width.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".