Managing Fatigue in Parkinson’s Disease: Protocol for a Pilot Randomized Controlled Trial
Bibliographic record
Abstract
Background. Fatigue is a disabling symptom of Parkinson’s disease (PD). Managing Fatigue: A Six-Week Energy Conservation Intervention was developed to improve the occupational performance of people with fatigue. Efficacy of this program has not been established in PD. Purpose. This study will assess feasibility of the Managing Fatigue: Individual Program ( MFIP ) delivered via videoconference, the Randomized Controlled Trial (RCT) protocol, and the preliminary effectiveness of the MFIP. Methods. A mixed-methods approach nested in a pilot RCT, randomizing 54 participants 1:1 to usual care or MFIP arms, will be employed to evaluate the feasibility and preliminary effectiveness of MFIP. Qualitative and quantitative data will be collected simultaneously. Implications. Results will identify evidence for establishing protocol requirements for a full-scale RCT. Knowledge of the effectiveness of the one-to-one videoconference delivery format of the program has the potential to enhance the accessibility and the quality of care of the PD population.
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 | 0.041 | 0.044 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.117 | 0.013 |
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".