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Record W2514270586 · doi:10.4103/1596-4078.190001

Prevalence of symptoms of self-reported knee osteoarthritis in Odo-Ogbe community, Ile-Ife

2016· article· en· W2514270586 on OpenAlexaboutno aff
AO Ojoawo, AO Oyeniran, MOB Olaogun

Bibliographic record

VenueNigerian Journal of Health Sciences · 2016
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoarthritisMedicinePhysical therapyAlternative medicinePathology

Abstract

fetched live from OpenAlex

Background: Osteoarthritis, (OA) the most common of all the types of arthritis, is a significant public health problem which contributes greatly to disability in the elderly. Community-based prevalence studies of OA in South-Western Nigeria were scanty for referencing. Objective: This study investigated the prevalence of symptoms of self-reported knee OA (KOA) in a heterogeneous community of Odo-Ogbe in Ile-Ife, South-Western Nigeria. Methods: All houses in Odo-Ogbe community were numbered, and all odd numbered houses were selected for the study. Every adult individual of aged 35 years and above living in the selected houses were recruited for the study. The total number of participants was 119 individuals and all of them participated in the study by completing Western Ontario and McMaster Universities Osteoarthritis Index Questionnaire. Their anthropometric variables were also measured. Data were analyzed using descriptive and inferential statistics. Results: There were 99 females and 20 males respondents that participated in the study. Forty-seven (39.5%) had knee pain and other KOA symptoms. Among those with KOA symptoms, six of them were males while 41 (87.2%) of them were females. There was a significant negative relationship (P Conclusion: The prevalence of symptomatic KOA at Odo-Ogbe community is high, more female were affected, and many of those affected had family history of arthritis.

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)0.005
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 score0.176
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.022
GPT teacher head0.323
Teacher spread0.301 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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