Abstract 1294: Cancer related fatigue and the additive effect of treatment in the context of lymphoma and CLL: An analysis of Lymphoma Coalition’s 2022 Global Patient Survey
Bibliographic record
Abstract
Abstract Introduction: Cancer related fatigue (CRF) continues to be a challenging phenomenon that is often underreported 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 sought to explore CRF in a large multinational cohort of patients with lymphoma and Chronic lymphocytic leukemia (CLL). Methods: Lymphoma Coalition deployed the Global Patient Survey in 2022 targeting patients with lymphoma and CLL and their caregivers. Responses were collected from 8637 respondents over 79 countries. Respondents indicated if they experienced fatigue as a symptom, side-effect or both. Data were enumerated and segregated into the following categories: No Fatigue/Treated (A), No Symptom/No Treatment (B), Symptom/No Treatment (C), Symptom Only/Treated (D), Side-effect Only (E), Symptom and Side-Effect (F). Data were stratified across lymphoma subtypes and categorical percentages were computed. An approximation of the contribution of treatment to CRF was calculated using the following equation: (F) + (E) - (D). Results: Valid responses were obtained for 6758 respondents. The majority of respondents for most subtypes reported CRF as both a symptom and a side-effect of treatment. Across the 13 subtypes examined, the attributable incidence of CRF related to treatment was found to be highest in: DLBCL (58%), Hodgkin (54%), Peripheral T- Cell (53%) and Mantle Cell (50%). The subtypes with the lowest attributable incidence of CRF included: Chronic Lymphocytic Leukemia (3%), Hairy Cell (12%), Cutaneous (25%) and Marginal Zone (26%). Conclusions: The majority of patients in this study cited CRF as a significant issue. The calculated fraction of fatigue attributable to treatment suggests that certain treatments may have a larger effect on fatigue than others. A limitation of this study is that we are relying upon patient reported outcomes and we acknowledge that patients may be unable to reliably differentiate fatigue from being a symptom or a side-effect. Our future work in this area will seek to map the intensity of fatigue from diagnosis through treatment in order to get a better understanding of the dynamic nature of this phenomenon. However, we are of the opinion that the compounding effect of CRF related to both disease and treatment yields opportunities to individualize treatment to patients based upon their symptomatic CRF. Citation Format: Steve E. Kalloger, Amanda Watson, Shawn Sajkowski, Lorna Warwick. Cancer related fatigue and the additive effect of treatment in the context of lymphoma and CLL: An analysis of Lymphoma Coalition’s 2022 Global Patient Survey [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1294.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".