MétaCan
Menu
Back to cohort
Record W2077760085 · doi:10.1002/dmrr.716

Regenerative failure of diabetic nerves bridging transection injuries

2007· article· en· W2077760085 on OpenAlexafffund
Douglas W. Zochodne, Gui Fang Guo, Brooks Magnowski, Mohammed Bangash

Bibliographic record

VenueDiabetes/Metabolism Research and Reviews · 2007
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsOntario Brain InstituteUniversity of Calgary
FundersCanadian Institutes of Health ResearchAlberta Heritage Foundation for Medical ResearchCanadian Diabetes Association
KeywordsBridging (networking)MedicinePhysical medicine and rehabilitationComputer scienceComputer security

Abstract

fetched live from OpenAlex

BACKGROUND: Failed regeneration compounds the deficits imposed by diabetes from peripheral neuropathy. In this work, we addressed how diabetes or local glucose toxicity might impact peripheral nerve trunk regeneration and reconstitution across major sciatic nerve transection injuries of rats. METHODS: Specific conduits, amendable to manipulation of infused glucose concentrations through a T connection, were perfused with 5 or 30 mmol/L glucose in nondiabetics or 5 mmol/L glucose in rats with experimental diabetes. Quantitative early and later regenerative outgrowth was measured. RESULTS: Local glucose exposure had no impact on early axon or Schwann cell outgrowth or partnering nor later myelinated axon regeneration. Despite only mildly attenuated early sprouting of axons with Schwann cells, diabetic bridges exhibited massive later failure of reconstitution by 3 weeks after injury. CONCLUSION: Diabetes is associated with severe limitations in regenerative success, despite appropriate early axon outgrowth.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentallow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.022
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.079
GPT teacher head0.360
Teacher spread0.281 · 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

Labeled directly by 2 models reading the full record.

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

Citations29
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueDiabetes/Metabolism Research and ReviewsSame topicNerve injury and regenerationFrench-language works237,207