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Record W1966917087 · doi:10.2174/138945010790980303

Opioids and Cannabinoids Interactions: Involvement in Pain Management

2010· review· en· W1966917087 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

VenueCurrent Drug Targets · 2010
Typereview
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsHôtel-Dieu de Montréal
Fundersnot available
KeywordsCannabinoidOpioidAnalgesicNociceptionEndogenous opioidMechanism (biology)PharmacologyMedicineCannabinoid receptorDrugNeurosciencePsychologyReceptorInternal medicine

Abstract

fetched live from OpenAlex

Among several pharmacological properties, analgesia is the most common feature shared by either opioid or cannabinoid systems. Cannabinoids and opioids are distinct drug classes that have been historically used separately or in combination to treat different pain states. Indeed, it is widely known that activation of either opioid or cannabinoid systems produce antinociceptive properties in different pain models. Moreover, several biochemical, molecular and pharmacological studies support the existence of reciprocal interactions between both systems, suggesting a common underlying mechanism. Further studies have demonstrated that the endogenous opioid system could be involved in cannabinoid antinociception and recent data have also provided evidence for a role of the endogenous cannabinoid system in opioid antinociception. These interactions may lead to additive or even synergistic antinociceptive effects, emphasizing their clinical relevance in humans in order to enhance analgesic effects with lower doses and consequently fewer undesirable side effects. Thus, the present review is focused on bidirectional interactions between opioids and cannabinoids and their potent repercussions on pain modulation.

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: Review · Consensus signal: Review
Teacher disagreement score0.976
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
Bibliometrics0.0010.000
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 head0.043
GPT teacher head0.355
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