Progressive Supranuclear palsy (PSP) disease progression, management, and healthcare resource utilization: a retrospective observational study in the US and Canada
Bibliographic record
Abstract
BACKGROUND: Progressive supranuclear palsy (PSP) is a rare neurodegenerative brain disease with rapid progression and currently limited treatment options. A comprehensive understanding of disease progression, management, and healthcare resource utilization is limited, and further research is challenging due to the small population of patients. To address these challenges in conducting PSP research, individuals with PSP were recruited using a multichannel approach tailored specifically to the PSP community. We performed a retrospective observational study using data abstracted from participant medical records collected from multiple patient care centers. RESULTS: Seventy-two individuals with PSP were eligible for inclusion. On average, 144 medical documents per participant were collected from an average of 2.9 healthcare centers per participant, with a mean study period of 7.9 years. Among participants with a date of symptom onset documented in the medical records, the median time for the onset of the first fall was 2.0 years (IQR 3.2) before diagnosis, the median onset of unsteady gait or gait impairment was 1.2 years (IQR 1.8) before diagnosis, and the median onset of mobility problems was 0.8 years (IQR 1.8) before diagnosis. The most widely utilized healthcare resources, with at least 85% of participants using each of these resources at some point during the disease course, were medications (100%), imaging (99%), assistive devices (90%), supportive care (86%), and surgeries and procedures (85%). CONCLUSIONS: This retrospective study adds to the current understanding of PSP symptoms, comorbidities, and healthcare resource utilization (HRU) across the disease journey. By involving individuals with PSP and their caregivers or legally authorized representatives in the research process, this study was unique in its approach to participant recruitment and enabled individuals to participate in research without the need for travel. We collected medical documents from multiple healthcare centers, allowing for broad data collection covering the entire disease journey. This approach to the collection of real-world data may be used to generate valuable insights into many aspects of disease progression and management in PSP and many other rare diseases.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".