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Record W1981225176 · doi:10.1179/016164104225013798

Peripheral nerve regeneration through guidance tubes

2004· review· en· W1981225176 on OpenAlexaff
Jason S. Belkas, Molly S. Shoichet, Rajiv Midha

Bibliographic record

VenueNeurological Research · 2004
Typereview
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsRegeneration (biology)Peripheral nervePeripheralMedicineAnatomyBiomedical engineeringBiologyCell biologyInternal medicine

Abstract

fetched live from OpenAlex

Biological nerve grafts have been extensively utilized in the past to repair peripheral nerve injuries. More recently, the use of synthetic guidance tubes in repairing these injuries has gained in popularity. This review focuses on artificial conduits, nerve regeneration through them, and an account of various synthetic materials that comprise these tubes in experimental animal and clinical trials. It also lists and describes several biomaterial considerations one should regard when designing, developing, and manufacturing potential guidance channel candidates. In the future, it it likely that the most successful synthetic nerve conduit will be one that has been fabricated with some of these strategies in mind.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.370
GPT teacher head0.484
Teacher spread0.114 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations416
Published2004
Admission routes1
Has abstractyes

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