Residual limb neuropathic pain association with neuroma, prosthetic, function, and participation outcomes in individuals living with a transtibial amputation: an exploratory study
Bibliographic record
Abstract
OBJECTIVE: To determine the strength of the association between residual limb neuropathic pain intensity and the number of neuromas, prosthetic, functional, and participation outcomes, and assess whether ultrasound (US) biomarkers of neuromas differ between pain intensities. DESIGN: Cross-sectional study. SUBJECTS: Twenty-two participants with a transtibial amputation for more than 12 months, with and without residual limb neuropathic pain. METHODS: Participants completed questionnaires (Numerical Pain Rating Scale, Pain Disability Index (PDI), Prosthetic Profile of the Amputee-Locomotor Capabilities Index), and had their residual limbs examined by US. Whenever a neuroma was diagnosed during US, images of the neuroma(s) were recorded and US biomarkers were computed. RESULTS: Of the 27 neuromas diagnosed, pain intensity was associated with no use of walking aid, less daily prosthesis wearing time, a higher PDI score, and a neuroma at the common fibular nerve. The cross-sectional area, the thickness ratio, or the thickness of the overlying tissues was not associated with pain intensity. CONCLUSION: Though the results enrich currently available evidence on clinical variables potentially associated with the intensity of neuropathic pain in individuals living with a transtibial amputation, and on the limited value of US biomarkers studied to determine the association between neuroma(s) and pain intensity, future studies providing higher quality evidence remain needed.
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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 | 0.001 | 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".