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Record W1989405423 · doi:10.3821/1913-701x-142.1.43

What Patients Do Following a New Diagnosis of Knee Osteoarthritis

2009· article· en· W1989405423 on OpenAlexvenueno aff
Kelly Grindrod, Chiara Marra, Lindsey Colley, Bridgette Oteng, Louise Gastonguay, Ross T. Tsuyuki

Bibliographic record

VenueCanadian Pharmacists Journal / Revue des Pharmaciens du Canada · 2009
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineKnee painPhysical therapyOsteoarthritisBody mass indexOverweightAlternative medicineInternal medicine

Abstract

fetched live from OpenAlex

Objective To describe patient use of health services and products within 6 months of a new diagnosis of knee OA. Methods Patients with knee pain and no previous diagnosis of knee OA were recruited by community pharmacists using a simple questionnaire to determine the likelihood of knee OA. In total, 194 participants considered likely to have knee OA were referred to a rheumatologist for a standardized knee exam and radiograph. Of these, 190 were confirmed to have knee OA and were subsequently followed for a period of 6 months. At baseline, 1, 3 and 6 months, a survey was administered that contained questions designed to elicit information on participants' health service and product use. Categories included pain-relieving medication, exercise (e.g., walking), treatments/aids (e.g., knee taping/brace, acupuncture, Arthritis Society Management Program, shoe inserts) and supplements (e.g., glucosamine). In addition, to assess participant satisfaction with pharmacy services, participants were asked to rate their levels of agreement with 4 statements using a five-point Likert scale. Results Follow-up data for all 3 time points were available for 124 (65%) participants. Of these, 64% were female, the mean age was 63 years and 67% were considered either obese or overweight according to their body mass index (BMI). By 6 months, 93% had visited their primary care practitioner for OA, 74% had initiated exercise, 59% had started supplements, 51% had taken pain-relieving medications and 45% had used activity aids. When asked “who gave you this advice,” the majority of participants stated “on my own” for all categories. This was followed by “family physician” for all categories except supplements, which were recommended by “family/friend” 20% of the time. Participants selected “pharmacist” least frequently in all categories, including for the initiation of pain-relieving medication, where only 2% reported a pharmacist recommendation. Of those taking pain-relieving medications, 49% took NSAIDs, 28% took acetaminophen and 12% took a combination of both. The majority of participants were very satisfied with the pharmacy services received (>90%) and few reported any complaints (>10%). While 18% of participants thought that pharmacy services could be better, only 17% thought that pharmacy services were just about perfect. Conclusion Following a diagnosis of knee OA, the majority of participants sought out an intervention, the most popular being exercise and natural medicine supplements. Despite the initial diagnosis by a pharmacist and self-reported satisfaction with pharmacy services, few participants attributed interventions to pharmacists. Even with recent evidence showing that pharmacist involvement can reduce potentially dangerous NSAIDs use in knee OA, most participants appeared to make lifestyle changes independent of health professional advice, and pharmacists played a very small role in this. These results suggest that more work is needed to expand the role of pharmacists toward chronic disease management.

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)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.026
GPT teacher head0.289
Teacher spread0.263 · 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 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

Citations0
Published2009
Admission routes1
Has abstractyes

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