Do larger people have more naevi? Naevus frequency versus naevus density
Bibliographic record
Abstract
BACKGROUND: It is unclear which of the number or the density of naevi on the skin is the more appropriate measure of risk of melanoma. Furthermore, the relationship between the number of naevi and their density in an individual has not been explored. Thus, for example, it is unknown if larger people tend to have more naevi by virtue of having a larger skin area, or if the density of naevi is similar in people of different body sizes. In this study, we explored the relationship between the number and the density of naevi in a sample of adolescents. SUBJECTS AND METHODS: A sample survey of naevi in 472 grade 9 secondary school students (aged 14-15 years) was conducted in Tasmania, Australia during 1992, and a subset of these individuals was followed up in 1997. Counts of naevi of various sizes were taken on the arm, leg, and back. Naevus density was estimated by using an algorithm to estimate body surface area from the height and weight of an individual. More general relationships of the naevus counts to height and weight were also explored. Finally, we considered whether the relationship between naevus density and the anthropometric variables could be confounded by exposure to ultraviolet radiation. RESULTS: The mean number of naevi was very similar in the two samples. Naevus density was slightly lower in the 1997 sample, mainly because of increasing body size in the cohort. The numbers of naevi were only weakly related to height and weight in males, and there was essentially no relationship in females. Regression analysis showed significant relationships of weight to the back naevus counts in males in 1992 and 1997, and to the arm naevus count in males in 1997; otherwise, none of the regression coefficients for height and weight were statistically significant. This picture did not change following adjustment for potentially confounding variables indicating time spent outdoors or in the sun. Furthermore, there was no evidence that time spent in the sun was related to the body mass index. CONCLUSIONS: It appears that the number and density of naevi in an individual are unrelated. Accordingly, with the present state of knowledge concerning the risk of melanoma, both the number and density of naevi should be considered as equally valid in future studies as markers of the risk of melanoma, and in studies on the natural history of naevi. If the disease mechanism is systemic, and not related to particular naevi, naevus density might form the better marker of risk. However, if the disease mechanism is related to effects on particular naevi, then the risk would vary in proportion to the number of naevi.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.006 | 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 teacher head, 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".