Bibliographic record
Abstract
UNLABELLED: Physical activity (PA) is an important health behavior in almost any population but it may be particularly helpful for cancer survivors. OBJECTIVE: To introduce this special issue on PA in cancer survivors and to provide a summary of its important contributions to the field. METHODS: A brief historical review of PA research in cancer survivors followed by a narrative review of the articles published in this special issue. RESULTS: This special issue contains 13 original articles reporting 15 studies on PA in cancer survivors. Just over half of the studies focus on breast cancer survivors, whereas the remainder focus on understudied cancer survivor groups such as lung, ovarian, colorectal, prostate, hematologic, and pediatric. Moreover, a majority of the studies focus on the survivorship phase of the cancer continuum. Perhaps the most distinctive feature of this special issue is the number of studies focusing on the determinants of PA. Taken together, the 13 articles make significant contributions in four areas: (1) randomized controlled trials of PA interventions with supportive care endpoints, (2) observational studies on the determinants of PA, (3) observational studies on the 'determinants of PA determinants', and (4) studies on methodological and feasibility issues related to conducting PA trials. CONCLUSIONS: PA research is making an important contribution to the health and well-being of cancer survivors across the entire cancer control continuum. This special issue builds on this momentum and provides the single largest collective contribution of knowledge to date in the field of PA in cancer survivors.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".