Frequency and Localization of Neuromas in Transtibial Residual Limbs
Bibliographic record
Abstract
OBJECTIVE: To describe the frequency and localization of neuromas in residual limbs of individuals with transtibial amputation using ultrasound imaging. DESIGN: Cross-sectional study. SETTING: Rehabilitation center research laboratory. PARTICIPANTS: Adults who have lived with a transtibial amputation for >12 months were recruited for this study. Participants were included regardless of the presence or absence of residual limb neuropathic pain. Twenty-three participants (24 transtibial residual limbs) with and without residual limb neuropathic pain were enrolled. The etiology of amputation of most participants was peripheral vascular disease and diabetes. INTERVENTION: A comprehensive history was collected and a musculoskeletal ultrasound assessment for the presence and location of neuromas in their residual limb was conducted. During the ultrasound evaluation, a sonopalpation Tinel test was performed by applying pressure on each neuroma with the probe. MAIN OUTCOME MEASURES: Number of neuromas and their localization in each residual limb examined. RESULTS: A total of 31 neuromas in the 24 transtibial residual limbs were identified by ultrasound imaging. The average number of neuromas per residual limb was 1.3. All the major peripheral nerves studied could present neuromas, with a predominance of the superficial fibular nerve within our sample. Thirty-five percent of all the neuromas were described as painful. CONCLUSIONS: The presence of terminal neuromas on surgically sectioned nerves in transtibial residual limbs is frequent. Seventy-nine percent of participants had at least one neuroma. Ultrasound imaging is clinically useful to identify neuromas. The evaluator can easily communicate with the patient to diagnose symptomatic neuromas.
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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 | 0.002 |
| 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) | 0.002 | 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".