Living with leg lymphedema: developing a novel model of quality lymphedema care for cancer survivors
Bibliographic record
Abstract
PURPOSE: Lower-extremity lymphedema (LEL) is a lifelong consequence of cancer therapy and can lead to serious physical and psychosocial complications for many cancer survivors. However, clinical knowledge and treatment of LEL remain minimal. The purpose of this study was to integrate perspectives of lymphedema patients and healthcare providers (HCPs) on LEL to develop a novel model for quality lymphedema care. METHODS: A mixed-methods approach was implemented. Standardized questionnaires and semi-structured interviews were used to assess psychosocial well-being and experiences of LEL patients. Interviews were also used to evaluate the clinical experiences of HCPs working within tumour groups associated with cancer-related LEL. Thematic analysis was used to analyse qualitative data. RESULTS: Twenty-two patients and eleven HCPs participated in this study. Patient QOL, generalized anxiety and depressive symptom scores revealed a complex interplay between psychosocial well-being and supportive LEL care after cancer. Three themes emerged from interviews with patients (n = 19) and HCPs (n = 11): level of lymphedema knowledge, effectiveness of rehabilitation oncology services and barriers to care. IMPLICATIONS FOR CANCER SURVIVORS: We developed a novel model for quality lymphedema care that emphasizes the importance of continued physical and psychosocial support for LEL patients, while illustrating the importance of HCPs in facilitating a smooth transition for patients to LEL care after cancer treatment.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".