Modelling the Functional Comorbidity Index as a predictor of health-related quality of life in patients with glenoid labrum disorders
Bibliographic record
Abstract
Background/aim Health-related quality of life (HRQoL) is increasingly assessed within orthopaedic research. For those patients presenting with glenoid labral pathologies, there is little information on how baseline comorbidities affect long-term outcomes and HRQoL. This study aimed to investigate a model, including baseline comorbidities and demographics, to predict change in 2-year HRQoL scores in adult patients with glenoid labral tears or degenerations. Methods Participants provided Functional Comorbidity Index (FCI) scores and self-completed the Western Ontario Rotator Cuff (WORC) index at 6, 12 and 24 months. Univariable and multivariable linear regressions were performed to assess predictive quality of baseline comorbidities and demographics on the primary outcome measure of interest (change in WORC score). Results Multivariate regression with a continuous scaled FCI (β=617.8, p=0.042), age (by decade) (β=297, p<0.01), surgical group (β=−476.69, p<0.01) and an interaction term between FCI and age (β=−103.65, p=0.03) were significant predictors of change in WORC scores at 2-year follow-up (r2=0.293858). Multivariate regression with FCI scaled categorically reported only patients with three comorbidities (β=−454.06, p=0.057) and age (by decade) (β=156.87, p=0.04) as the only significant predictors of change in WORC scores at 2-year follow-up (r2=0.1279). Conclusion The continuous FCI model is better suited to predict future WORC and HRQoL scores among this patient population. Patients reporting with higher numbers of baseline comorbidities improved significantly more than patients with fewer comorbidities. This information on expected change in HRQoL scores among patients with a wide range of FCI scores at baseline may help guide treatment decisions based on these criteria.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".