Lipedema in Male Progressing to Subclinical and Clinical Systemic Lymphedema
Bibliographic record
Abstract
The aim of the present case study was to report the 3-year follow-up of a male patient with lipedema and subclinical systemic lymphedema evaluated using multi-segment bioimpedance. The report describes the case of a 53-year-old male with a history of oncological surgery involving lymph node clearance in the right inguinal region followed by radiotherapy and chemotherapy. The physical examination revealed lipedema and lymphedema in the right lower limb. The patient was submitted to multi-segment bioimpedance, circumference measurements and volumetry, with the detection of clinical lymphedema of the limb. The patient underwent intensive treatment for lymphedema using the Godoy Method ® , which resulted in a substantial reduction in the edema. However, at the 1-year follow-up, the patient had progressed to subclinical systemic lymphedema, followed a year later by clinical systemic lymphedema evaluated using multi-segment bioimpedance. Lipedema is less frequent in men compared to women, but the increase in weight is an aggravating factor in both sexes. This condition initially affects the lower limbs, progressing to subclinical systemic lymphedema, followed by clinical systemic lymphedema determined using multi-segment bioimpedance, demonstrating that edema in patients with lipedema may be systemic. J Med Cases. 2022;13(6):249-252 doi: https://doi.org/10.14740/jmc3806
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".