Plenary and Symposium Abstracts
Bibliographic record
Abstract
BACKGROUND: In 2008, the International Agency for Research on Cancer (IARC) released its World Cancer Report (IARC, 2008), which indicated that cancer accounts for approximately 12% of all-cause mortality worldwide.IARC estimated that globally 7.6 million peopled died from cancer and that 12.4 million new cases were diagnosed in 2008.The report went on to project that, due to increases in life expectancy, improvements in clinical diagnostics, and shifting trends in health behaviors (e.g., increases in smoking and seden-tary lifestyles), in the absence of significant efforts to improve global cancer control, cancer mortality could increase to 12.9 million and cancer incidence to 20 million by the year 2030.METHOD: Looking deeper into the data, it becomes clear that cancer-related stigma and myths about cancer are important problems that must be addressed, although different from a country to another.Stigmas about cancer present significant challenges to cancer control: stigma can have a silencing effect, whereby efforts to increase cancer awareness are negatively affected.he social, emotional, and financial devastation that all too often accompanies a diagnosis of cancer is, in large part, due to the cultural myths and taboos surrounding the disease.RESULTS: Combating stigma, myths, taboos, and overcoming silence will play important roles in changing this provisional trajectory.There are several reasons that cancer is stigmatized.Many people in our area perceived cancer to be a fatal disease.Cancer symptoms or body parts affected by the disease can cultivate stigma.Fears about treatment can also fuel stigma.There was evidence of myths associated with cancer, such as the belief that cancer is contagious, or cancer may be seen as a punishment.CONCLUSIONS: After reviewing these different examples of Cultural Myths and Taboos met in cancer Care, we can report these lessons learned
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.524 | 0.320 |
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".