Understanding the Lexicon of Fatigue in Parkinson’s Disease
Bibliographic record
Abstract
BACKGROUND: Fatigue in Parkinson's disease (PD) is multifaceted and associated with reduced quality of life. In turn, the language used by people with PD to describe fatigue is variable and poorly understood. We sought to elucidate the lexicon of fatigue using a qualitative grounded theory approach. OBJECTIVE: The objective of this study was to understand how patients with PD describe fatigue. METHODS: A pre-study phase of online journaling (Phase 1) provided information regarding topics of importance to patients. Following this, two independent samples of fatigued subjects were studied. Individuals with PD participated in a telephone interview (Phase 2); interview transcripts were analyzed to develop a detailed codebook. To ensure trustworthiness of the findings, an online survey (Phase 3) was administered to individuals with self-reported PD participating in the online study Fox Insight. The survey included the following question: "How do you define fatigue? Please provide your definition in the space below." The codebook developed from Phase 2 was applied to the Phase 3 responses. RESULTS: Fifteen individuals participated in Phase 2 and 413 individuals completed Phase 3. Fatigue was subdivided into three domains: cognitive, emotional, and physical. Nearly all individuals experienced more than one domain of fatigue. The most common themes included tiredness, lack of energy, and negative motivation. CONCLUSION: Fatigue in PD is multidimensional. Questionnaires that only assess the physical impact of fatigue may not be adequate to capture the broad range of experiences of fatigue among people with PD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".