The GLA:D® Canada program for knee and hip osteoarthritis: A comprehensive profile of program participants from 2017 to 2022
Bibliographic record
Abstract
BACKGROUND: The Good Life with osteoArthritis in Denmark (GLA:D®) program was implemented in Canada in 2017 with the aim of making treatment guideline-recommended care available to the 4 million Canadians with knee and hip osteoarthritis (OA). This report describes the GLA:D® Canada program, registry and data collection procedures, and summarizes the sociodemographic and clinical profile of participants with knee and hip OA to inform the scientific research community of the availability of these data for future investigations and collaborations. METHODS: The GLA:D® program consists of three standardized components: a training course for health care providers, a group-based patient education and exercise therapy program, and a participant data registry. Patients seeking care for knee or hip OA symptoms and enrolling in GLA:D® are given the option to provide data to the GLA:D® Canada registry. Participants agreeing to provide data complete a pre-program survey and are followed up after 3-, and 12-months. Data collected on the pre-program and follow-up surveys include sociodemographic factors, clinical characteristics, health status measures, and objective physical function tests. These variables were selected to capture information across relevant health constructs and for future research investigations. RESULTS: At 2022 year-end, a total of 15,193 (11,228 knee; 3,965 hip) participants were included in the GLA:D® Canada registry with 7,527 (knee; 67.0%) and 2,798 (hip; 70.6%) providing pre-program data. Participants were 66 years of age on average, predominately female, and overweight or obese. Typically, participants had knee or hip problems for multiple years prior to initiating GLA:D®, multiple symptomatic knee and hip joints, and at least one medical comorbidity. Before starting the program, the average pain intensity was 5 out of 10, with approximately 2 out of 3 participants using pain medication and 1 in 3 participants reporting a desire to have joint surgery. Likewise, 9 out 10 participants report having previously been given a diagnosis of OA, with 9 out 10 also reporting having had a radiograph, of which approximately 87% reported the radiograph showed signs of OA. CONCLUSION: We have described the GLA:D® Canada program, registry and data collection procedures, and provided a detailed summary to date of the profiles of participants with knee and hip OA. These individual participant data have the potential to be linked with local health administrative data registries and comparatively assessed with other international GLA:D® registries. Researchers are invited to make use of these rich datasets and participate in collaborative endeavours to tackle questions of Canadian and global importance for a large and growing clinical population of individuals with hip and knee OA.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".