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Record W2588394296 · doi:10.5312/wjo.v8.i2.187

Knee osteoarthritis: Therapeutic alternatives in primary care

2017· article· en· W2588394296 on OpenAlexaff
Allison L Evaniew, Nathan Evaniew

Bibliographic record

VenueWorld Journal of Orthopedics · 2017
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineOsteoarthritisGuidelinePhysical therapyPrimary careKnee painQuality of life (healthcare)Health carePopulationPublic healthArthritisMEDLINEObesityAlternative medicineIntensive care medicineFamily medicineNursingInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

AIM: To discusses pharmacological and non-pharmacological therapeutic alternatives for managing knee osteoarthritis in primary care by primary health care nurse practitioners. METHODS: A case example is presented, the evidence-based guideline recommendations of the Osteoarthritis Research Society International and the American Academy of Orthopaedic Surgeons are reviewed, and a plan of care is developed. RESULTS: Osteoarthritis is the most common form of arthritis seen in primary care, and it is a major public health issue because the aging population and widespread obesity have drastically increased incidence. Osteoarthritis is clinically associated with escalating chronic pain, physical disability, and decreased quality of life. Early diagnosis of mild osteoarthritis in relatively young patients presents an opportunity for primary health care providers to manage pain, increase quality of life, and decrease risk of disability. CONCLUSION: Primary health care providers can implement these recommendations in their own practices to provide care to patients with knee osteoarthritis based on current best evidence.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.285
Teacher spread0.266 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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