Measurement of limb volume: Laser scanning versus volume displacement
Bibliographic record
Abstract
BACKGROUND: Determining the prevalence and treatment success of surgical lymphedema requires accurate and reproducible measurement. A new method of measurement of limb volume is described. METHODS: A series of inanimate objects of known and unknown volume was measured using digital laser scanning and water displacement. A similar comparison was made with 10 human volunteers. Digital scanning was evaluated by comparison to the established method of water displacement, then to itself to determine reproducibility of measurement. RESULTS: (1) Objects of known volume: Laser scanning accurately measured the calculated volume but water displacement became less accurate as the size of the object increased. (2) Objects of unknown volume: As average volume increased, there was an increasing bias of underestimation of volume by the water displacement method. The coefficient of reproducibility of water displacement was 83.44 ml. In contrast, the reproducibility of the digital scanning method was 19.0 ml. (3) Human data: The mean difference between water displacement volume and laser scanning volume was 151.7 ml (SD +/- 189.5). The coefficient of reproducibility of water displacement was 450.8 ml whereas for laser scanning it was 174 ml. CONCLUSION: Laser scanning is an innovative method of measuring tissue volume that combines precision and reproducibility and may have clinical utility for measuring 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".