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Record W1513707253 · doi:10.1002/9781444329711.ch18

Other Pharmacological Agents

2010· other· en· W1513707253 on OpenAlexaff
Philip Peng, Mary Mary Lynch Lynch

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMedicine
TopicInflammatory mediators and NSAID effects
Canadian institutionsQueen Elizabeth II Health Sciences CentreUniversity Health NetworkUniversity of TorontoDalhousie UniversityToronto Western HospitalMount Sinai Hospital
Fundersnot available
KeywordsMedicineAnalgesic agentsAcetaminophenAnalgesicChronic painCannabinoidAcute painIntensive care medicinePharmacologyAnesthesiaPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

This chapter reviews several other agents that have not been covered in previous chapters. The conventional non-steroidal anti-inflammatory agents (NSAIDs) and acetaminophen are widely available and helpful for mild to moderate pain. When pain is at more severe levels these agents are often inadequate on their own for treatment of chronic pain. Muscle relaxants have been investigated and are primarily indicated for acute rather than chronic use in the management of musculoskeletal pain. There are a growing number of controlled trials examining cannabinoid agonists in the treatment of pain. Based on current evidence supporting that cannabinoids are analgesic and safe, it is reasonable to use a cannabinoid as a second or third line agent either as a single agent or in combination with other agents exhibiting a different mechanism of action.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.077
Threshold uncertainty score0.258

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0770.046

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 head0.020
GPT teacher head0.319
Teacher spread0.299 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2010
Admission routes1
Has abstractyes

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