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Record W2148122022

Managing osteoarthritis. Medication use among seniors in the community.

2004· article· en· W2148122022 on OpenAlexaffabout
Beverley Lawson, Wayne Putnam, Kelly Nicol, G. C. Archibald, Jim Mackillop, Howard Conter, Dawn Frail

Bibliographic record

VenuePubMed · 2004
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineMedical prescriptionSelf-medicationNova scotiaFamily medicineOsteoarthritisAlternative medicineHealth careNursing
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine what types of medication seniors in the community were using to manage osteoarthritis (OA). DESIGN: Mailed self-administered survey. SETTING: Three family medicine community practice sites in cities in Nova Scotia. PARTICIPANTS: All seniors (aged 65 and older) on the electronic record of each practice site with a physician-confirmed diagnosis of OA (N = 244). MAIN OUTCOME MEASURES: Proportion of seniors using prescribed medications or self-care products (nonprescribed medications and herbal and natural health products) for OA. RESULTS: Response rate was 78%. About 15% were using no medication, 74% were using at least one type of self-care product (60% were using nonprescribed medications, and 45% were using herbal and natural health products), and 52% were using prescribed medications alone or in combination with self-care products. CONCLUSION: Seniors' use of prescribed and self-care products for OA is very high. Physicians must be aware that patients seeking prescriptions likely are also using self-care products. The potential for drug interactions is high; patients should be made aware of the risks associated with taking multiple products.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score0.262

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 head0.055
GPT teacher head0.282
Teacher spread0.227 · 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 designObservational
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

Citations13
Published2004
Admission routes2
Has abstractyes

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