Upper extremity lymphatic function at rest and during exercise in breast cancer survivors with and without lymphedema compared with healthy controls
Bibliographic record
Abstract
Lymphoscintigraphy was used to measure lymphatic function at rest and during exercise in breast cancer survivors with lymphedema (BCRL, n = 10), breast cancer survivors (BC, n = 10), and controls (Cont, n = 10). After injection of (99m)Tc-antimony colloid to the hands, subjects rested or performed 12 repeated sets of arm cranking for 2.5 min at 0.6 W/kg followed by 2.5 min of rest. One-minute spot views were taken with a gamma-radiation camera immediately postinjection and every 10 min over 60 min to calculate clearance rate. As well, an upper body scan was taken at 65 min postinjection to measure radiopharmaceutical uptake in the axilla (Ax) and forearm (Fore). All groups displayed similar increases in clearance rate with exercise (P = 0.000). Ax significantly increased with exercise in Cont only [Cont: (mean +/- SD) 4.9 +/- 2.6 vs. 7.9 +/- 4.2%, P = 0.000; BCRL: 1.4 +/- 1.2 vs. 1.7 +/- 2.1%, P = 0.531; BC: 3.9 +/- 3.4 vs. 5.2 +/- 3.2%, P = 0.130], whereas Fore, indicating dermal backflow, significantly increased in BCRL only (BCRL: 2.4 +/- 0.87 vs. 4.4 +/- 2.0%, P = 0.004; BC: 1.1 +/- 0.25 vs. 1.1 +/- 0.31%, P = 0.784; Cont: 0.93 +/- 0.26 vs. 1.0 +/- 0.20%, P = 0.296). The results indicate that, in women with BCRL, exercise causes radiopharmaceuticals to clear from the hand at the same rate as BC and Cont, but, instead of reaching the axilla, a greater amount of activity gets trapped in the dermis of the forearm. BC, meanwhile, have similar lymphatic function as Cont; however, there is a highly variable response that may suggest that some BC subjects may be at risk for developing lymphedema.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".