Frailty Trajectories and Influencing Factors in Patients With Non-Hodgkin Lymphoma During Chemotherapy: Protocol for a Longitudinal Mixed Methods Study
Bibliographic record
Abstract
Background: High-intensity chemotherapy for non-Hodgkin lymphoma (NHL) has been shown to improve survival outcomes. However, its substantial toxicity may induce or exacerbate patient frailty, which is strongly associated with treatment interruptions, increased adverse effects, and reduced survival rates. Despite this, the dynamic progression of frailty and its specific contributing factors among patients with NHL undergoing chemotherapy remain poorly understood. Objective: This study aims to investigate the heterogeneity of frailty trajectories and explore their risk factors in patients with NHL. We seek to generate insights into the dynamic relationship between frailty and disease course. The findings may offer health care professionals dynamic insights into frailty progression and could help inform strategies for the early identification of high-risk populations through the systematic assessment of key factors. Ultimately, this work seeks to contribute to the evidence base for developing interventions that could mitigate or prevent frailty. Methods: This longitudinal mixed methods study will recruit 250 patients newly diagnosed with NHL from multicenter hospitals in China. Quantitative data will be collected at 3 time points: before chemotherapy, during the third cycle of chemotherapy, and at the end of chemotherapy. We will use validated questionnaires (ie, the Tilburg Frailty Indicator) to gather information on sociodemographics, frailty, cognition, physical condition, health literacy, anxiety, and nutrition. Qualitative data will be collected via semistructured interviews and observations at the end of chemotherapy. The growth mixture model and logistic regression analysis will be used to analyze quantitative data, and the diachronic analysis method and the directed content analysis method will be used for the qualitative data. Both types of data will be analyzed in parallel and separately. Finally, we will integrate the datasets to identify areas of confirmation, complementation, or discordance. Results: The research protocol and informed consent form were approved by the Medical Ethics Committee of the First Affiliated Hospital of Henan University of Science and Technology (2024-03-K171). Recruitment began in January 2025, with 168 participants enrolled as of September 2025. The data collection and analysis processes are expected to be finalized by March 2026. Data management is still ongoing; therefore, data analysis has not yet been conducted. Conclusions: As a pilot trial, this research is primarily designed to assess the feasibility of our methods and generate preliminary data on frailty progression. The findings could offer initial insights and help inform the development of future strategies for the early identification of high-risk populations through systematic screening, with the ultimate goal of informing interventions that may prevent frailty.
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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 | 0.038 | 0.029 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.006 |
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".