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Record W2105231474 · doi:10.1177/107327480000700201

Neurophysiology of Cancer Pain

2000· review· en· W2105231474 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

VenueCancer Control · 2000
Typereview
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsToronto Western HospitalUniversity Health Network
Fundersnot available
KeywordsMedicineCancer painCancerNeuroanatomyNeurophysiologyNeuropathic painPain controlPain medicineNeurosciencePsychiatryAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Recent basic science research has greatly added to our knowledge of pain mechanisms. Application of this knowledge to cancer pain syndromes has led to new and innovative approaches to cancer pain management. METHODS: The mechanisms involved in the three main cancer pain syndromes (somatic, visceral, and neuropathic) are reviewed, and various therapeutic options are discussed. RESULTS: Advances in knowledge in neurophysiology, neuroanatomy, and pharmacology have allowed a greater understanding of the peripheral and central mechanisms of pain. New drugs and interventional techniques based on this knowledge have improved the control of cancer pain. CONCLUSIONS: Understanding the neurophysiology of cancer pain promotes use of the most appropriate palliative measures for pain control.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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)0.0020.000

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 head0.030
GPT teacher head0.347
Teacher spread0.317 · 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