Cancer-Related Fatigue and the Additive Effect of Treatment in the Context of Lymphoma: An Analysis of the Lymphoma Coalition’s 2022 Global Patient Survey
Bibliographic record
Abstract
Cancer-related fatigue (CRF) continues to be a challenging phenomenon that is often under-reported and poorly understood. With etiologies in both disease and treatment manifesting as a symptom and a side effect respectively, CRF is highly incident and presents a significant clinical problem that impacts survivorship. We conducted a survey to ascertain the patient reported incidence of symptoms and side effects for people with lymphoma or chronic lymphocytic leukemia. We found that CRF was enhanced in those who received more intense therapies that coincided with more aggressive lymphoma subtypes. These data illuminate an unmet need among patients with lymphoma and provides an opportunity to further refine treatment regimens to reduce the burden of CRF in this vulnerable population. SIGNIFICANCE: CRF is a highly incident phenomenon in lymphoma that can be ascribed to a combination of causes. We have demonstrated substantial variability across various subtypes of lymphoma and have estimated that nearly half of the reported fatigue comes from treatment. Increased screening for and monitoring of fatigue will yield favorable health-related quality of life that will benefit health technology assessment activities and yield improved outcomes for patients.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".