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Record W337395571

An evidence-based approach to prescribing NSAIDs in the treatment of osteoarthritis and rheumatoid arthritis: The Second Canadian Consensus Conference.

2000· article· en· W337395571 on OpenAlexaffabout
H Tannenbaum, P M Peloso, Anthony S. Russell, Bernard Marlow

Bibliographic record

VenuePubMed · 2000
Typearticle
Languageen
FieldMedicine
TopicInflammatory mediators and NSAID effects
Canadian institutionsMcGill UniversityMontreal General Hospital
Fundersnot available
KeywordsMedicineRheumatoid arthritisOsteoarthritisNonsteroidalIntensive care medicineClinical trialAlternative medicineHeart failureAdverse effectWarfarinRandomized controlled trialPhysical therapyInternal medicineAtrial fibrillation
DOInot available

Abstract

fetched live from OpenAlex

The Second Canadian Consensus Conference was convened to discuss the latest developments in the management of osteoarthritis (OA) and rheumatoid arthritis (RA), and to make evidence-based recommendations, specifically regarding the use of nonsteroidal anti-inflammatory drugs (NSAIDs) for these indications in primary care practice. The recent availability of cyclo-oxygenase-2-specific inhibitors has raised questions as to their role in the pharmacological management of OA and RA, particularly in relation to conventional treatments such as acetaminophen and nonspecific NSAIDs (with or without misoprostol or proton pump inhibitors). The recommendations in this document, which were arrived at through critical review of data from published randomized, clinical trials, deal with treatments of choice, information to discuss with patients, use of NSAIDs in patients at risk for serious upper gastrointestinal complications, renal or hepatic impairment or congestive heart failure, appropriate follow-up, and the use of NSAIDs with anti- hypertensives, warfarin, low dose acetylsalicylic acid and other medications. The goal of these recommendations is to improve patient outcomes in the primary care setting by maximizing treatment efficacy and minimizing rates of adverse events.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.961
Threshold uncertainty score0.996

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 head0.034
GPT teacher head0.230
Teacher spread0.195 · 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 designOther design
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

Citations49
Published2000
Admission routes2
Has abstractyes

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