Evidence on definitions, concepts, outcome instruments, and interventions for chronic fatigue in spinal cord injury: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to review the existing evidence on definitions, concepts, measurement instruments, and interventions for chronic fatigue in spinal cord injury. INTRODUCTION: Chronic fatigue in people with spinal cord injury is an under-studied issue that affects between 25% and 56.6% of people with spinal cord injury. There are questions about how it is defined and managed due to its complex, multifactorial nature and relationship with related conditions. No overview of chronic fatigue in spinal cord injury exists and we are in need of a shared definition of chronic fatigue, as well as a comprehensive review of concepts and evidence supporting outcome instruments and interventions. INCLUSION CRITERIA: This review will include empirical and non-empirical studies that focus on definitions, concepts, measurement instruments, and interventions for chronic fatigue in spinal cord injury. Studies that focus on peripheral muscle fatigue will only be included if they include chronic fatigue as a secondary outcome. METHODS: This review will be done in three phases. Phase I will provide an overview of definitions of chronic fatigue in spinal cord injury and will include a qualitative analysis of concept attributes and characteristics. Phase II will focus on factors related to chronic fatigue and measurement instruments used to measure chronic fatigue, and phase III will focus on interventions. Full texts will be screened by two independent reviewers against inclusion criteria. Results will be presented in tabular form with a narrative summary.
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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 | 0.070 | 0.076 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.010 |
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".