Exploring Fatigue Trajectories in Early Symptomatic Knee and Hip Osteoarthritis: 6-year Results from the CHECK Study
Bibliographic record
Abstract
OBJECTIVE: To examine whether different groups of fatigue trajectories can be identified among patients with early symptomatic osteoarthritis (OA) of the knee or hip, to describe the level of fatigue severity within each of these fatigue groups, and to investigate the involvement of age, sex, use of medication, comorbidity, and OA severity in relation to group membership. METHODS: Six years of followup data on fatigue (Medical Outcomes Study Short Form-36 Vitality scale) came from the Cohort Hip and Cohort Knee (CHECK) cohort. Growth mixture modeling was applied to identify distinct fatigue trajectories as well as to take into account the effects of the patient characteristics. RESULTS: Three fatigue trajectories were identified: low fatigue, low-to-high fatigue, and high fatigue. Latter trajectories showed considerable overlap from years 2 to 6, but differed in some patient characteristics in comparison with each other and in comparison with the low fatigue group. Comorbidity, medication use, and sex were significantly associated with the identified trajectories. Women, individuals with a comorbid disease, and those who used medication were more likely to follow a high fatigue trajectory. CONCLUSION: These findings suggest heterogeneous development of fatigue in the early OA population associated with varying patient characteristics. Further, this study shows that a considerable number of patients with OA already experience elevated levels of fatigue at an early stage of OA. While these findings need to be replicated, the identification of these trajectories with differing patient characteristics may warrant tailored psychosocial interventions for patients with elevated levels of fatigue.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".