JoVE Video Dataset
Bibliographic record
Abstract
Over the past decade, the field of prosthetics has witnessed significant progress, particularly in the development of surgical techniques to enhance the functionality of prosthetic limbs. Notably, novel surgical interventions have had an additional positive outcome, as individuals with amputations have reported neuropathic pain relief after undergoing such procedures. Subsequently, surgical techniques have gained increased prominence in the treatment of postamputation pain, including one such surgical advancement - targeted muscle reinnervation (TMR). TMR involves a surgical approach that reroutes severed nerves as a type of nerve transfer to "target" motor nerves and their accompanying motor end plates within nearby muscles. This technique originally aimed to create new myoelectric sites for amplified electromyography (EMG) signals to enhance prosthetic intuitive control. Subsequent work showed that TMR also could prevent the formation of painful neuromas as well as reduce postamputation neuropathic pain (e.g., Residual and Phantom Limb Pain). Indeed, multiple studies have demonstrated TMR's effectiveness in mitigating postamputation pain as well as improving prosthetic functional outcomes. However, technical variations in the procedure have been identified as it is adopted by clinics worldwide. The purpose of this article is to provide a detailed step-by-step description of the TMR procedure, serving as the foundation for an international, randomized controlled trial (ClinicalTrials.gov, NCT05009394), including nine clinics in seven countries. In this trial, TMR and two other surgical techniques for managing postamputation pain will be evaluated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.384 | 0.247 |
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".