Reduction of Lymphedema Using Complete Decongestive Therapy: Roles of Prior Radiation Therapy and Extent of Axillary Dissection
Bibliographic record
Abstract
Although radiation therapy (RT) contributes to lymphedema (LE), it is unknown whether RT contributes to more difficulty (more treatments) or less success (decreased LE reduction) with therapy for established LE. We reviewed the results of complete decongestive therapy (CDT) for LE with respect to a history of RT and the number of lymph nodes dissected. Breast cancer survivors with LE were referred to CDT-certified therapists. CDT consists of treatment (phase 1) and maintenance (phase 2) phases. During phase 1, the patient meets with a therapist daily until the LE reduction plateaus; then phase 2 (self-care) begins. During phase 1, LE is quantified weekly at a minimum. Fifty-three patients underwent CDT and completed phase 1. The median number of treatments to plateau was 12 (range 6-25); the median limb volume reduction was 36% (-4-119%). Thirty-six patients with an RT history had an insignificant difference in LE reduction (p = .49) and the number of sessions to plateau (p = .54) compared with 17 patients without RT. The median examined number of nodes was 12 (range 3-28). No significant correlation was observed between the number of nodes examined and percent reduction (r = -.390); no significant correlation (r = .291; critical r = .396 for p = .05 for both cases) was observed between the number of nodes sampled and the number of sessions to plateau. Patients with LE obtained relief regardless of whether they received surgery or surgery plus RT. The insignificant correlation between the number of lymph nodes and percent reduction could become significant with a larger sample size.
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".