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Record W2990485268 · doi:10.4103/njhs.njhs_14_17

Influence of pain intensity and difficulty on health-related quality of life of patients with knee osteoarthritis

2017· article· en· W2990485268 on OpenAlexaboutno aff
OA Ojoawo, O. C. Falade, EB Arayombo

Bibliographic record

VenueNigerian Journal of Health Sciences · 2017
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoarthritisMedicinePhysical therapyQuality of life (healthcare)Health related quality of lifeDescriptive statisticsKnee painIntensity (physics)Alternative medicineDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Background: Knee osteoarthritis (OA) is one of the most common causes of pain and disability in the western world and it affects up to 80% of people over the age of 65. Aim: The objective of this study was to examine the influence of self-reported symptoms of knee OA (KOA) on the health-related quality of life (HRQoL) of patients with KOA. Materials and Methods: Seventy patients diagnosed with KOA were purposively recruited for the study. The Western Ontario and McMaster Universities OA Index was used to assess the pain intensity, functional difficulty and stiffness, whereas HRQoL form Short-Form 12 Health Survey was used to assess the quality of life of patients with KOA. The data collected were analysed using SPSS version 17. Descriptive and inferential statistics were used to summarise the data. Results: There was a statistically significant negative relationship between age and physical difficulty (r = −0.301 P Conclusion: It was concluded from the study that in patients with KOA, the higher the pain intensity and/or physical difficulty, the lower the patient's HRQoL.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.077
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.028
GPT teacher head0.302
Teacher spread0.274 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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