Cyclic patterns of incidence rate for skin malignant melanoma: association with heliogeophysical activity
Bibliographic record
Abstract
BACKGROUND: Our previous studies revealed cyclicity in the incidence rate of skin malignant melanoma (SMM; ICD9, Dx:172) in the Czech Republic (period T=7.50-7.63 years), UK (T=11.00 years) and Bulgaria (T=12.20 years). Incidences compared with the sunspot index Rz (lag-period dT=+2, +4, +6, +10 or +12 years) have indicated that maximal rates are most likely to appear on descending slopes of the 11-year solar cycle, i.e., out of phase. We summarized and explored more deeply these cyclic variations and discussed their possible associations with heliogeophysical activity (HGA) components exhibiting similar cyclicity. METHODS: Annual incidences of SMM from 5 countries (Czech Republic, UK, Bulgaria, USA and Canada) over various time spans during the years 1964-1992 were analyzed and their correlations with cyclic Rz (sunspot number) and aa (planetary geomagnetic activity) indices were summarized. Periodogram regression analysis with trigonometric approximation and phase-correlation analysis were applied. RESULTS: Previous findings on SMM for the Czech Republic, UK and Bulgaria have been validated, and cyclic patterns have been revealed for USA (T=8.63 years, P<0.05) and Canada (Ontario, T=9.91 years, P<0.10). Also, various 'hypercycles' were established (T=45.5, 42.0, 48.25, 34.5 and 26.5 years, respectively) describing long-term cyclic incidence patterns. The association of SMM for USA and Canada with Rz (dT=+6 and +7 years, respectively) and aa (dT=-10 and +9 years, respectively) was described. Possible interactions of cyclic non-photic influences (UV irradiation, Schumann resonance signal, low-frequency geomagnetic fluctuations) with brain waves absorbance, neuronal calcium dynamics, neuro-endocrine axis modulation, melatonin/serotonin disbalance and skin neuro-immunity impairment as likely causal pathways in melanoma appearance, were also discussed. CONCLUSION: The above findings on cyclicity and temporal association of SMM with cyclic environmental factors could not only allow for better forecasting models but also lead to a better understanding of melanoma aetiology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".