Physical exercise and immune system function in cancer survivors
Bibliographic record
Abstract
BACKGROUND: There are a limited number of interventions for cancer survivors following completion of primary therapy that might reduce the risk of cancer recurrence and/or secondary malignancies and increase survival times. It has been proposed that physical exercise may be beneficial by enhancing the anticancer immune system response. The purpose of the current article is to: 1) briefly describe the immune system response to tumors, 2) discuss the impact of anticancer therapy on immune system function in cancer survivors, 3) provide a systematic and comprehensive review of the extant literature examining physical exercise and immune system function in cancer survivors, and 4) offer a critical analysis of this literature and outline directions for future research. METHODS: A comprehensive literature search up to March 2001 identified empirical articles that examined the effects of physical exercise training on immune system function in cancer survivors from CD-ROM database searches and manual searches. RESULTS: To the authors' knowledge, six empirical studies published between 1994 and 2000 have examined physical exercise and immune system function in cancer survivors. Overall, four out of six studies reported statistically significant improvements in a number of cancer-related immune system components as a result of exercise. However, there are several limitations that must be considered when interpreting the findings of these studies. These limitations involve the samples, designs, physical exercise interventions, physical fitness assessments, and immunologic assessments. CONCLUSIONS: Additional research is needed to determine if physical exercise in cancer survivors may reduce the risk of cancer recurrence and secondary malignancies and increase survival times.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".