Screening cut-off scores for clinically significant fatigue in early Parkinson’s disease
Bibliographic record
Abstract
Background: Fatigue is one of the most disabling non-motor symptoms in PD. Researchers have previously used cut-offs validated in non-PD conditions when using the Fatigue Severity Scale (FSS) or the Multidimensional Fatigue Inventory (MFI) scores to evaluate fatigue in PD. Objective: We used a set of criteria for diagnosing clinically significant fatigue in PD to identify the proper cut-offs of the FSS and MFI. Methods: One hundred thirty-one PD patients (59F; age 67.3 ± 7.6 y; H&Y 1.6 ± 0.7) were assessed for clinically significant fatigue, followed by the FSS, MFI, Center for Epidemiologic Studies Depression Scale (CES-D), and Montreal Cognitive Assessment (MOCA). Mean scores were compared between 17 patients who met diagnostic criteria (significant fatigue group, SFG) and 114 who did not (non-significant fatigue group, NSFG). Results: =.1) than the NSFG. Using area under the curve (AUC) of receiver operating characteristic (ROC) analyses, we recommend the following cut-offs: 9-item FSS 37; total MFI 60; general fatigue 11; reduced activity 10; physical fatigue 9; mental fatigue 9; and reduced motivation 9. Conclusions: The recommended cut-offs for clinically significant fatigue in the FSS, MFI, and MFI dimensions will be valuable for diagnosing clinically significant fatigue and for future studies in investigating pathophysiology and potential treatments of fatigue in PD.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".