Outcome measures used in peripheral nerve surgery for symptomatic neuroma in upper extremity amputations: A scoping review
Bibliographic record
Abstract
Novel surgical treatments for painful neuromas are increasingly used, but determining which provides the greatest benefit has been difficult due to the inconsistent use of outcome measures. We mapped the current literature of outcome measures used to evaluate peripheral nerve surgery for the management of symptomatic neuromas in patients who underwent an adult-acquired upper extremity amputation (UEA). Medline, Embase, Cochrane, and CINAHL were searched for primary research written in the English language from inception to February 2023. The search yielded 1137 articles, of which 35 were included for final analysis. Studies varied in their assessment of pain, health-related quality of life (HRQOL), neurotrophic measures, psychological and sensorimotor function, highlighting a consensus on crucial domains but also revealing significant heterogeneity in the use and application of outcome measures among primary studies. Our findings highlight the need to establish common standards that reflect the best evidence and unique needs of the UEA population. This includes developing a core outcome set, utilizing multi-center trials, and maintaining flexibility to adapt to ongoing advancements in patient-reported outcome measures (PROMs) research.
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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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".