Prevalence of symptoms of self-reported knee osteoarthritis in Odo-Ogbe community, Ile-Ife
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".