A Simple and Innovative Device to Measure Arm Volume at Home for Patients With Lymphedema After Breast Cancer
Bibliographic record
Abstract
PURPOSE: We designed an arm volumeter specifically for home use based on the water displacement method. The objective of this study was to determine its accuracy and precision, and compare it with a standard volumeter used in lymphedema clinics worldwide. PATIENTS AND METHODS: Using a standard model hospital volumeter and our own device, we took three consecutive measurements of 11 specially cast cylinders, which had known volumes ranging from 10 mL to 4 L, and measurements of both arms of 15 volunteers. RESULTS: Measurements with both volumeters were highly accurate (R2 = 0.9999) when compared with the known volumes of the cast cylinders, and were strongly correlated (R2 = 0.9974) when each arm volume was compared between volumeters. Measurements with our volumeter were more precise both with the cylinders (average standard deviation [SD], 3.2 v 8 mL; P = .0553) and with the arms (average SD, 11.1 v 19 mL; P = .0034). Whereas the standard volumeter is expensive, fragile (acrylic), and prone to leaks, our volumeter is inexpensive, virtually indestructible, leak proof, and suitable for home use. CONCLUSION: Arm volumes can be measured quickly and accurately at home using a simple, inexpensive, and robust device based on water displacement. Readily accessible arm volumetry at home may have widespread influence on the management of lymphedema after breast cancer.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".