Head and Neck Cancer in Pan-American Notable People: An International Survey
Bibliographic record
Abstract
Background: The study of notable people as advocates for raising cancer awareness began in the latter decades of the 20th century. This research aimed to identify Pan-American notable people with head and neck cancer (HNC) and to explore senior health professionals’ perspectives on communicating stories of notable patients with HNC to promote prevention. Method: A cross-sectional survey was conducted using an online questionnaire designed in REDCap and administered to 32 senior health professionals with long-standing academic and clinical backgrounds in HNC. In addition, a structured literature review was performed on PubMed, Scopus, EMBASE, Web of Science, LILACS, and gray literature. Results: 18 notable figures were successfully identified from the survey, and 24 from the literature review. These individuals came from the United States, Brazil, Argentina, Mexico, El Salvador, Chile, Colombia, and Peru, and were recognized primarily for their performances as actors, artists, musicians, and athletes. The professionals’ outlooks were positive, with 31 (96.9%) agreeing that disseminating these stories can contribute to reducing risk behaviors. Furthermore, all participants (100%) agreed that such stories can promote early detection of HNC, primarily through social media, followed by the internet, and television. Conclusions: The study identified notable individuals and gathered positive perspectives from professionals. Our results suggest that notable people could serve as potential advocates for HNC prevention. Further research is warranted to explore the potential of this prevention strategy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".