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Record W2965236981 · doi:10.3166/dea-2019-0065

Sending out Biased Signals: an Appropriate Proposition for Pain?

2019· article· en· W2965236981 on OpenAlexafffund
Élie Besserer‐Offroy, Philippe Sarret

Bibliographic record

VenueDouleur et Analgésie · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPharmacological Receptor Mechanisms and Effects
Canadian institutionsUniversité de SherbrookeMcGill University
FundersCanadian Institutes of Health Research
KeywordsMorphineFood and drug administrationOpioidAgonistMedicineμ-opioid receptorOpioid receptorPropositionAnesthesiaPharmacologyPsychologyReceptorInternal medicinePhilosophy

Abstract

fetched live from OpenAlex

In the past few years, several biased ligands acting at the mu-opioid receptor were reported in the literature. These agonists are aimed at reducing pain while having fewer side effects than morphine, the gold standard of opioid analgesics. In this mini-review, we describe and discuss the recent advances in mu-biased ligands actually in preclinical and clinical development stages, including the latest U.S. Food and Drug Administration review of oliceridine, a biased mu-agonist for moderate to severe acute pain treatment developed by the company Trevena.

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

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 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 score0.051
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.025
GPT teacher head0.319
Teacher spread0.293 · 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

Machine predicted; a candidate call from one teacher head, not a consensus.

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

Citations3
Published2019
Admission routes2
Has abstractyes

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