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Pathophysiology and Animal Models of Cancer-Related Painful Peripheral Neuropathy

2010· review· en· W2147758491 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Oncologist · 2010
Typereview
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsMcGill University
FundersNational Institute of Neurological Disorders and StrokeNational Institutes of HealthLouise and Alan Edwards FoundationCanada Research Chairs
KeywordsMedicinePathophysiologyPeripheral neuropathyNeuropathic painCancerChemotherapyBioinformaticsInflammationNeurosciencePathologyAnesthesiaSurgeryInternal medicineDiabetes mellitus

Abstract

fetched live from OpenAlex

There are undoubtedly several causes of painful peripheral neuropathy in cancer patients. Some mechanisms are directly attributable to the tumor; others lie with the therapy, be it surgery, radiation, or chemotherapy. Several animal models have been developed to study the pathophysiological mechanisms that contribute to neuropathic pain. These include inflammation-based models, nerve trauma-induced models, and chemotherapy-induced models of neuropathic pain. My colleagues and I recently identified abnormalities in mitochondrial structure and function in peripheral sensory fibers that are associated with neuropathic pain induced by common chemotherapeutic agents and that can be reversed by agents that enhance mitochondrial function. Our hope is that further identification and clarification of the pathophysiological mechanisms involved at the periphery will help us to develop new classes of medicines and treatment options.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.054
GPT teacher head0.366
Teacher spread0.312 · 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