Bibliographic record
Abstract
In this article, featured in the CEBP Focus on Cancer Survivorship Research, Parry and colleagues report on the confluence of the increased size and higher age of the cancer survivor population. The authors collected cancer incidence and prevalence data from 9 registries through the Surveillance, Epidemiology, and End Results Program. They report that, as of January 2008, the number of cancer survivors is estimated at 11.9 million. In addition, approximately 60% of these survivors are age 65 or older, and by the year 2020, anestimated 63% of cancer survivors will be 65 or older. This important study shows the convergence of improved cancer survival and population aging, resulting in a growing population of older adult cancer survivors with unique survivorship needs.Considerable interindividual variability exists with regard to the risk of developing an adverse outcome for a given cancer therapeutic. Bhatia presents an overview of the role of genomic variation in the risk of therapyrelated complications. The article discusses common outcomes associated with therapeutic exposures, including cardiomyopathy, obesity, osteonecrosis, ototoxicity, and subsequent malignancies. Issues such as study design, definition of endpoints, and a reliable plan for collecting and maintaining highquality DNA samples are important factors for determining how genetic variation contributes to cancer therapy-related complications.Fatalistic beliefs about cancer have been implicated in low uptake of screening and delays in presentation, particularly in individuals with low socioeconomic status (SES). To explore the interrelationship among SES, fatalism, and early cancer detection behaviors, Beeken and colleagues interviewed adults in the United Kingdom. They report that fatalism was associated with being less positive about early cancer detection and more fearful about seeking help for a suspicious symptom. The authors also found that lower SES groups were more fatalistic. This study promotes addressing fatalistic beliefs about cancer, which might be particularly important for lower SES groups.Indigenous populations in Canada and abroad have poorer survival after a breast cancer diagnosis compared with their geographical counterparts. To explore the reasons for this disparity, Sheppard and colleagues used the Ontario Cancer Registry to compare survival after diagnosis in First Nations (FN) women with that of Non-FN women. Although the authors found that survival was more than 3 times poorer for FN women diagnosed at stage I, compared with non-FN women, the risk of death after a breast cancer diagnosis was nearly 5 times higher among FN women with a comorbidity. These findings suggest that having a preexisting comorbidity was the most important factor in explaining the breast cancer survival disparity among FN women.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.272 | 0.125 |
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".