Identifying Trajectories of Pain Severity in Early Symptomatic Knee Osteoarthritis: A 5-year Followup of the Cohort Hip and Cohort Knee (CHECK) Study
Bibliographic record
Abstract
OBJECTIVE: To identify subgroups of pain trajectories in patients with symptomatic knee osteoarthritis (OA), and to explain these different trajectories by patient characteristics, lifestyle, and coping factors, as well as radiographic features. METHODS: Longitudinal data of pain severity (0-10) from 5 years of followup of the CHECK (Cohort Hip and Cohort Knee) study was used. Latent class growth analysis identified homogeneous subgroups with distinct trajectories of pain. Multinomial regression analysis was used to examine different lifestyle and coping characteristics between the trajectories. RESULTS: In longitudinal pain data of 5 years of followup in 705 participants, 3 pain trajectories were identified: marginal, mild, and moderate pain trajectories. Compared with the marginal pain trajectory, the mild and moderate pain trajectories can be characterized by the following baseline variables: body mass index (BMI) > 25, additional hip pain, low education level, using the coping strategy "worrying," and having ≥ 3 comorbidities. Moderate pain trajectory can be supplemented with the Kellgren-Lawrence grading scale grade ≥ 2 radiological change. CONCLUSION: Three trajectories of pain were identified. Participants with a BMI > 25, secondary school as highest education level, having at least 3 comorbidities, additional hip pain, and/or whose coping style is worrying are more likely to develop a moderate or mild pain trajectory compared with those without these characteristics. In the management of knee pain in people with early symptomatic OA, attention should also be given to additional factors such as hip pain, other comorbidities, passive coping strategy, and obesity.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".