A Lymphedema Self-Management Programme: Report on 30 Cases
Bibliographic record
Abstract
PURPOSE: Compression therapy is the most important element in the treatment and long-term management of moderate and severe lymphedema, but it is not universally accessible in Canada. For those unable to access private lymphedema treatment, physiotherapists at the McGill University Health Centre (MUHC) Lymphedema Clinic began teaching patients and caregivers how to use compression bandages safely and effectively. METHODS: A retrospective chart review was combined with structured telephone or in-person interviews with 30 people who had attended a self-bandaging clinic in the MUHC Lymphedema Clinic between 2011 and 2012. Patients were monitored weekly until limb volume plateaued, and a compression garment was then fitted for ongoing maintenance. Monthly or quarterly surveillance continued for 1 year. Follow-up interviews were conducted 3 to 18 months after patients had received their garments. RESULTS: The majority of participants had moderate to severe lymphedema; all achieved reduction of edema in the range of 48% to 92%. More than three-quarters of participants reported a global rate of change (GRC) of ≥80%. Themes derived from the interviews included the importance of bandaging, the feeling of being in control, and difficulties with compression garments. Participants spontaneously expressed satisfaction about having tools to manage their condition themselves. CONCLUSION: For selected patients with lymphedema, a self-bandaging programme can be a route to lymphedema reduction, independence, and self-efficacy.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".