Description and prediction of physical functional disability in psoriatic arthritis: A longitudinal analysis using a Markov model approach
Bibliographic record
Abstract
OBJECTIVE: To describe the longitudinal course of physical functioning in patients with psoriatic arthritis. METHODS: Between June 1993 and June 2003, 341 patients attending the University of Toronto Psoriatic Arthritis Clinic completed 2 or more Health Assessment Questionnaires (HAQs). At the time of administration of each HAQ, patients were assigned to 1 of 3 physical functional disability states, based on their HAQ score. A Markov model that allowed for transitions to and from these 3 disability states was used to characterize the longitudinal course of physical functioning, as well as to identify factors for both progression and regression of disability. RESULTS: Despite patient variability in the course of physical functioning, the following 3 longitudinal patterns were observed: 1) a stable state of disability throughout the entire study period, with 28%, 12%, and 6% of patients experiencing no, moderate, or severe disability, respectively; 2) a steady improvement or deterioration in disability over time (this pattern was observed in 27% of patients); and 3) a fluctuating state of disability, occurring in 27% of the patients. Sex, age, disease duration, number of actively inflamed joints, and number of deformed joints predicted transitions between disability states. CONCLUSION: Although 28% of patients appeared resistant to becoming disabled over the duration of this study, the remaining patients were observed either to experience enduring disability or to move between disability states.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".