Latent transition analysis of pain phenotypes in people at risk of knee osteoarthritis: The MOST cohort study
Bibliographic record
Abstract
OBJECTIVE: Pain phenotypes (PP) have been identified across different stages of knee osteoarthritis (KOA) and understanding the stability of PPs prior to the development of symptomatic KOA can help to inform preventative strategies. We aimed to identify PPs and their transitions in people without radiographic KOA and profile participant characteristics. DESIGN: Data from 5 - (T1), 7- (T2) and 12-year (T3) visits from the Multicenter Osteoarthritis Study (MOST) were used. Individuals with Kellgren-Lawrence grade 0 and knee pain ≤30/100 at T1 were sampled. PP variables included pressure pain thresholds, temporal summation (with method changed at T3), depressive symptoms, pain catastrophizing, sleep quality, and widespread pain. Latent Transition Analysis using Bayesian Information Criteria informed class numbers and transitions. Unconstrained, constrained, and modified constrained models (conditional response probabilities fixed for indicator variables except for TS) were compared for fit. Participant characteristics were used to profile class membership. RESULTS: 348 individuals (59% females), mean age (SD): 59.3 (6.7) were included. The optimal model fit for data across T1-T3 was a "modified" constrained model with 3 classes (class 1: low pain burden, class 2: high pain sensitization, class 3: high psychological burden). Classes were similar over time except for the increased probability of TS at T3. Most (86%) participants remained in the same class; only 14% transitioned overtime. CONCLUSION: Distinct PPs were identified in those at risk of KOA that remained stable over time, suggesting these trait-like features may require consideration for comprehensive management of the symptom experience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".