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Record W2498631371 · doi:10.1385/0-89603-129-2:145

Techniques for Assessing the Effects of Drugs on Nociceptive Responses

2003· book-chapter· en· W2498631371 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

VenuePsychopharmacology · 2003
Typebook-chapter
Languageen
FieldVeterinary
TopicVeterinary Pharmacology and Anesthesia
Canadian institutionsMcGill University
Fundersnot available
KeywordsNalorphineAnalgesicContext (archaeology)MorphineMedicineAspirinDrugPharmacologyTail flick testNociceptionAnesthesiaInternal medicineOpioid(+)-NaloxoneBiologyReceptor

Abstract

fetched live from OpenAlex

Animal pain tests have been developed primarily for the screening of potential analgesic drugs. In this context, the most important characteristic of a test is that it correctly identify compounds that are analgesic in pathological pain in humans and correctly eliminate compounds without this activity (Taber, 1974 206; Frazer and Harris, 1967 92; Jacob, 1966 128). By this standard, the tail-flick test of D'Amour and Smith (1941) 56 is very accurate in detecting morphine-like drugs, though it is less successful with nalorphine-like drugs and very insensitive to aspirin-like drugs. Other tests have different profiles of sensitivity, and although there seems to be no ideal single laboratory test procedure for the evaluation of analgesics, a combination of several tests has considerable power to identify analgesics (Wood, 1984 237).

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.585
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.050
GPT teacher head0.411
Teacher spread0.361 · 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