Heterogeneity in health status and the influence of patient characteristics across patients seeking musculoskeletal orthopaedic care – a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Health status is an important predictor of patient outcomes. Consequently, identifying patient predictors of health status is essential. In musculoskeletal orthopaedic care, the majority of work examining the association between patient characteristics and health status has been undertaken among hip/knee cohorts. We investigate these associations comparing findings across four musculoskeletal cohorts (hip/knee; foot/ankle; neck/back; elbow/shoulder). METHODS: Patients seeking elective musculoskeletal orthopaedic care were recruited prior to consultation. Questionnaires captured health domain status (bodily pain, physical functioning, and mental and general health) and covariates: demographics; socioeconomic characteristics; and comorbidity. Scores were compared across cohorts. Two path regression analyses were undertaken. First, domain scores were simultaneously examined as dependent variables in the overall sample. Subsequently, the model was assessed stratified by cohort. RESULTS: 1,948 patients: 454 neck/back, 767 hip/knee, 378 shoulder/elbow, 349 foot/ankle. From stratified analyses, significant variability in covariate effects was observed. Worse bodily pain scores were associated with increasing age and female sex among hip/knee, low income among foot/ankle, and overweight/obese for foot/ankle and hip/knee. Worse mental health scores were associated with low income across cohorts except elbow/shoulder, low education within neck/back, and compared to Whites, Blacks had significantly worse scores among foot/ankle, better scores among hip/knee. Worse general health scores were observed for Asians among hip/knee, Blacks among foot/ankle, and South-Asians among elbow/shoulder and neck/back. CONCLUSION: The substantial heterogeneity across musculoskeletal cohorts suggests that patient- and cohort-specific approaches to patient counsel and care may be more effective for achieving optimal health and outcomes.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".