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Record W4205281319 · doi:10.3410/f.729085733.793562535

Faculty Opinions recommendation of Targeted muscle reinnervation to improve pain, prosthetic tolerance, and bioprosthetic outcomes in the amputee.

2019· dataset· en· W4205281319 on OpenAlexaff
Joel Katz

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2019
Typedataset
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsYork University
FundersNorthwestern University
KeywordsReinnervationNeuromaMedicineAmputationPhantom painPhantom limbPhysical medicine and rehabilitationChronic painNeuropathic painPhysical therapyAnesthesiaSurgery

Abstract

fetched live from OpenAlex

Scope and Significance: There are *185,000 amputations each year and nearly 2 million amputees currently living in the United States.Approximately 25% of these amputees will experience chronic pain issues secondary to localized neuroma pain and/or phantom limb pain.Problem: The significant discomfort caused by neuroma and phantom limb pain interferes with prosthesis wear, subjecting amputees to the additional physical and psychological morbidity associated with chronic immobility.Although numerous neuroma treatments are described, none of these methods are consistently effective in eliminating symptoms.Translational Relevance: Targeted muscle reinnervation (TMR) is a surgical technique involving the transfer of residual peripheral nerves to redundant target muscle motor nerves, restoring physiological continuity and encouraging organized nerve regeneration to decrease and potentially prevent the chaotic and misdirected nerve growth, which can contribute to pain experienced within the residual limb.Clinical Relevance: TMR represents one of the more promising treatments for neuroma pain.Prior research into ''secondary'' TMR performed in a delayed manner after amputation has shown great improvement in treating amputee pain issues because of peripheral nerve dysfunction.''Primary'' TMR performed at the time of amputation suggests that it may prevent neuroma formation while avoiding the risks associated with a delayed procedure.In addition, TMR permits the target muscles to act as bioamplifiers to direct bioprosthetic control and function.Summary: TMR has the potential to treat pain from neuromas while enabling amputee patients to return to their activities of daily living and improve prosthetic use and tolerance.Recent research in the areas of secondary (i.e., delayed) and primary TMR aims to optimize efficacy and efficiency and demonstrates great potential for establishing a new standard of care for amputees.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.390
Threshold uncertainty score0.871

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0050.002
Open science0.0010.001
Research integrity0.0060.003
Insufficient payload (model declined to judge)0.3900.204

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.

Opus teacher head0.026
GPT teacher head0.335
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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