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Record W2058270548 · doi:10.2146/ajhp060366

Improving rheumatoid arthritis outcomes: How do we get there?

2006· article· en· W2058270548 on OpenAlexaff
Jane Brown, Carlo A. Marra, Frank Pucino, Beth H. Resman‐Targoff

Bibliographic record

VenueAmerican Journal of Health-System Pharmacy · 2006
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsArthritis Research Centre of CanadaVancouver Coastal Health Research InstituteUniversity of British Columbia
Fundersnot available
KeywordsMedicineDermatologyRheumatoid arthritisIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

The symposium concluded with a panel discussion that reprised major points and provided additional insights. J. Richard Brown, Pharm.D., BCPS, FASHP, symposium chair, served as moderator. Highlights of the discussion are presented below. Patients with rheumatoid arthritis (RA) often have sicca symptoms that are very difficult to deal with, especially dry mouth and the Sjögren’s syndrome-like presentation. Is there any treatment that can be offered to these patients? There are some artificial local agents and muscarinic agonists, such as pilocarpine and cevimeline, that can stimulate saliva production, but that’s about all I’m aware of for those symptoms. Any orifice with a mucous membrane—the eyes, nose, mouth, vagina—as well as the skin, can be affected by sicca syndrome, and symptoms can be significant. Conjunctivitis can be a problem sometimes, and it requires topical treatment. Unfortunately, there haven’t been any major medical breakthroughs; therefore, the primary treatment goal is symptom management. Researchers at the National Institutes of Health are studying biopsy specimens of salivary glands to determine if there are any potential therapeutic targets, but results are not yet available.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.311
Teacher spread0.294 · 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.

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

Citations0
Published2006
Admission routes1
Has abstractyes

Explore more

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