Investigating musculoskeletal health and wellbeing; a cohort study protocol
Bibliographic record
Abstract
BACKGROUND: In an ageing population, pain, frailty and disability frequently coexist across a wide range of musculoskeletal diagnoses, but their associations remain incompletely understood. The Investigating Musculoskeletal Health and Wellbeing (IMH&W) study aims to measure and characterise the development and progression of pain, frailty and disability, and to identify discrete subgroups and their associations. The survey will form a longitudinal context for nested research, permitting targeted recruitment of participants for qualitative, observational and interventional studies; helping to understand recruitment bias in clinical studies; and providing a source cohort for cohort randomised controlled trials. METHODS: IMH&W will comprise a prospective cohort of 10,000 adults recruited through primary and secondary care, and through non-clinical settings. Data collection will be at baseline, and then through annual follow-ups for 4 years. Questionnaires will address demographic characteristics, pain severity (0-10 Numerical Rating Scale), pain distribution (reported on a body Manikin), pain quality (McGill Pain Questionnaire), central aspects of pain (CAP-Knee), frailty and disability (based on Fried criteria and the FRAIL questionnaire), and fracture risk. Baseline characteristics, progression and associations of frailty, pain and disability will be determined. Discrete subgroups and trajectories will be sought by latent class analysis. Recruitment bias will be explored by comparing participants in nested studies with the eligible IMH&W population. DISCUSSION: IMH&W will elucidate associations and progression of pain, frailty and disability. It will enable identification of people at risk of poor musculoskeletal health and wellbeing outcomes who might be suitable for specific interventions, and facilitate generalisation and comparison of research outcomes between target populations. The study will benefit from a large sample size and will recruit from diverse regions across the UK. Purposive recruitment will enrich the cohort with people with MSK problems with high representation of elderly and unwell people. TRIAL REGISTRATION: Clinicaltrials.gov NCT03696134. Date of Registration: 04 October 2018.
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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 | 0.069 | 0.047 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.062 | 0.025 |
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".