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Record W4382936735 · doi:10.1097/prs.0000000000010911

Comparison of 2 Regenerative Peripheral Nerve Interface Techniques for the Treatment of Rat Neuroma Pain

2023· article· en· W4382936735 on OpenAlexafffund
Jenna‐Lynn Senger, Aline B. Thorkelsson, Bonnie Y. Wang, K. Ming Chan, Stephen W.P. Kemp, Christine A. Webber

Bibliographic record

VenuePlastic & Reconstructive Surgery · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsUniversity of AlbertaUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMedicineNeuromaInlayPeripheralPeripheral nerveSurgeryAnatomyInternal medicineDentistry

Abstract

fetched live from OpenAlex

SUMMARY: Treatment of painful neuromas has long posed a significant challenge for peripheral nerve patients. The regenerative peripheral nerve interface (RPNI) provides the transected nerve with a muscle graft target to prevent neuroma formation. Discrepancies in RPNI surgical techniques between animal models ("inlay" RPNI) and clinical studies ("burrito" RPNI) preclude direct translation of results from bench to bedside and may account for variabilities in patient outcomes. The authors compared outcomes of these 2 surgical techniques in a rodent model. Animals treated with burrito RPNI after tibial nerve neuroma formation demonstrated no improvement in pain assessment, and tissue analysis revealed complete atrophy of the muscle graft with neuroma recurrence. By contrast, animals treated with inlay RPNI had significant improvement in pain with viable muscle grafts. The results suggest superiority of the inlay RPNI surgical technique for the management of painful neuroma in rodents. CLINICAL RELEVANCE STATEMENT: RPNIs are currently being used to prevent and treat neuroma and phantom limb pain. This preclinical study suggests the superiority of one surgical technique over the other.

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 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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.072
GPT teacher head0.328
Teacher spread0.256 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations4
Published2023
Admission routes2
Has abstractyes

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