Prevalence study of sun protection behaviors/practices in several representative population samples in Indonesia
Bibliographic record
Abstract
Med J Indones Non-melanoma skin cancer (NMSC) is the most common type of cancer in white population, and its incidence has increased worldwide over the last few decades.r-aThis increasing trend might be in part ascribed to such recently changed environmental and socio-cultural conditions as depletion of the atmospheric ozone layer, increasing aged population (in developed countries in particular), increasing outdoor recreational activities at shadeless sites, increasing concem about NMSC among the public at large which resulted in much earlier hospital visits, and earlier diagnosis/ recognition by medical professionals who have become more aware of NMSC.-AbstrakKanker kulit non-melanoma (KKNM) yang agak sering ditemukan dan tersebar di berbagai negara di dunia, diperkirakan paling ntungkin dicegah dengan perubah p erilaku./praktek berlindung dari prevalensi adalah penting u,1tuk m attributable risk percentaqe" (PA ntelakuknn perilaku perlindungan bersama dengan Rasio Odds un dalam runius perhitut'tgan, sJperti diuraikant secara terperinci dalam bagian kedua dari makalah ini.Sebelum kami dapat p,roporsi yang clapat ntu n khas untuk kelompok etnik dari KKNM oLeh tiap ktek'perlindungon t"rho In i mengajukan 10 pedoman sederhana untuk membantu risiio terjadiiya kanker di erika Serikat, karena dipandang berguna untuk Indonesia sebagai pedoman sementara untuk pencegahnn KKNM.
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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) | 0.001 | 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".