Mapping the intersection of social status and comorbidity in knee osteoarthritis: a WOMAC-based study
Bibliographic record
Abstract
Abstract Knee osteoarthritis (OA) is a disabling joint condition that leads to extreme mobility and quality of life impairment, particularly among older adults. This study aimed to investigate the socio-demographic factors and comorbid conditions influencing the severity of symptoms of knee OA using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Data were derived from 622 patients across 9 months from the major healthcare facilities of Dhaka. Age, sex, educational status, obesity, diabetes mellitus, and cardiovascular disease (CVD) were predictors for the severity of symptoms of OA of the knee, the study claimed. Female participants were more prone to have severe symptoms compared to males, and those who were more than 70-years-old were at greater risk of severe symptoms. Low educational status, obesity, diabetes mellitus, and CVD were also predictors for severe OA of the knee. Age (p<0.001), obesity (p<0.001), and diabetes (p<0.001) were the best predictors of severity of symptoms based on the multinomial logistic regression analysis. The findings from the study highlight the complex etiology of OA of the knee and the need for integral healthcare measures that address both the socio-economic and the physical determinants. Focused interventions need to be employed, particularly for high-risk groups such as the elderly, women, and the comorbid, to minimize the incidence of OA of the knee and maximize the outcomes for patients in settings such as that of Bangladesh, where resources may not be available.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".