Utilization of Rehabilitation Services After Idiopathic Inflammatory Myopathy Diagnosis: A Retrospective Cohort Assessment in the United States
Bibliographic record
Abstract
OBJECTIVE: To investigate utilization and timing of rehabilitation services for patients with idiopathic inflammatory myopathies (IIM). METHODS: This was a retrospective analysis of US patients in the TriNetX dataset. Patients were defined by at least 2 IIM International Classification of Diseases, 9th/10th revision (ICD-9/10) codes between 1 and 12 months apart with at least 1 year of preceding data (from prior non-IIM ICD codes or Current Procedural Terminology [CPT] codes). Recurrent event modeling was performed from first IIM ICD code to the event of a CPT code for physical therapy (PT), occupational therapy (OT), and speech language pathology (SLP). Analysis was adjusted for the following potential confounders: age at first IIM ICD code, sex, IIM diagnosis subtype, stroke, hospitalization, orthopedic procedure, hip fracture, and glucocorticoid use. RESULTS: In total, 22,434 patients with IIM and available data were identified, of whom 29.4% were male. After the first IIM ICD code, 20.1%, 5.3%, and 3.7% of patients had at least 1 PT, OT, and/or SLP evaluation, respectively, over an average of 10 years' follow-up. Mean time to rehabilitation services use was just under 2 years after first IIM code. Older age and prior use of the respective rehabilitation service were associated with higher rehabilitation service use across all categories. The dermatomyositis ICD group had significantly lower PT and OT use compared with the polymyositis ICD group. CONCLUSION: Rehabilitation utilization was infrequent overall, occurring nearly 2 years after the initial IIM ICD code. This may indicate barriers to appropriate integration of rehabilitation services for patients with IIM.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".