Reliability and validity of the Bangla version of the knee injury and osteoarthritis outcome score
Bibliographic record
Abstract
Background: Patient self-assessed outcome scores for musculoskeletal conditions are limited in Bangladesh, especially for knee osteoarthritis. Therefore, a reliable outcome measure like the widely used knee injury and osteoarthritis outcome score (KOOS) for Bangladeshi population is necessary. The aim was to assess the validity and reliability of the Bangla version of KOOS. Methods: Following Beaton et al. forward-backwards method, an expert committee translated and adapted the original English version of KOOS with slight modifications for the Bangladeshi Bangla-speaking population. The psychometric testing assessed the questionnaire's reliability using internal consistency (Cronbach’s alpha) and test-retest reliability (Intraclass correlation coefficients). The questionnaire was compared to validated Bangla versions of the Short-Form 36 health survey (SF-36) and the Western Ontario and McMaster Universities Arthritis Index (WOMAC) to establish construct validity. Results: This study involved 150 patients with knee osteoarthritis. Bangla KOOS was found to have good internal consistency (0.77-0.88) and high test-retest reliability (0.86-0.99). Construct validity was established by comparing Bangla KOOS with the WOMAC and SF-36. The Bangla KOOS sub-scores showed negative correlations with WOMAC domains (ρ = −0.41 to −0.93) and positive correlations with SF-36 domains (ρ = 0.26 to 0.68). Conclusions: Findings showed that the Bangla KOOS is a reliable and valid measure for evaluating outcomes in Bangladeshi patients with knee osteoarthritis. It is a dependable and valid outcome measure tailored to the local language
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".