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Record W3103611961 · doi:10.1097/gox.0000000000003132

TMRpni: Combining Two Peripheral Nerve Management Techniques

2020· article· en· W3103611961 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2020
Typearticle
Languageen
FieldMedicine
TopicNerve Injury and Rehabilitation
Canadian institutionsWestern University
Fundersnot available
KeywordsReinnervationMedicineNeuropathic painSensory systemPeripheral nervePeripheralPhysical medicine and rehabilitationMotor nerveSurgeryAnesthesiaAnatomyNeuroscienceInternal medicinePsychology

Abstract

fetched live from OpenAlex

Amputee patients suffer high rates of chronic neuropathic pain, residual limb dysfunction, and disability. Recently, targeted muscle reinnervation (TMR) and regenerative peripheral nerve interface (RPNI) are 2 techniques that have been advocated for such patients, given their ability to maximize intuitive prosthetic function while also minimizing neuropathic pain, such as residual and phantom limb pain. However, there remains room to further improve outcomes for our residual limb patients and patients suffering from symptomatic end neuromas. "TMRpni" is a nerve management technique that leverages beneficial elements described for both TMR and RPNI. TMRpni involves coaptation of a sensory or mixed sensory/motor nerve to a nearby motor nerve branch (ie, a nerve transfer), as performed in traditional TMR surgeries. Additionally, the typically mismatched nerve coaptation is wrapped with an autologous free muscle graft that is akin to an RPNI. The authors herein describe the "TMRpni" technique and illustrate a case where this technique was employed.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.029
GPT teacher head0.307
Teacher spread0.278 · 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