Understanding the Experience and Impacts of Brain Fog in Chronic Pain: A Scoping Review
Bibliographic record
Abstract
Introduction: Approximately 15% to 40% of persons with chronic pain as a primary disorder experience brain fog. Prior research has investigated the etiology of "brain fog" in conditions in which pain presents as a key feature (e.g., fibromyalgia). However, it remains understudied in the context of chronic 10 musculoskeletal pain. Following current scoping review guidelines, we obtained stakeholder input from patient and health care professionals (HCPs) to define this phenomenon. Specific aims of this review were to (1) identify factors contributing to brain fog, (2) identify the functional correlates of brain fog and assessments used to measure them, and (3) establish a definition of brain fog that can be employed by researchers and HCPs to advance research and care. Methods: A scoping review was conducted using recommendations of the Joanna Briggs Institute methodology of scoping reviews and the Levac et al methodology. Embase, Cinahl, PsycINFO, and Medline was searched to identify relevant sources. Findings were verified with patient and healthcare professionals. Results: We identified four 15 key features of brain fog: perceived variability, subjective cognitive dysfunction, participation limitations, and changes in functional activities. We developed a model of brain fog illustrating the overlapping categories of contributors to brain fog in chronic musculoskeletal pain: (1) neuroanatomical and neurophysiological, (2) mental health/emotional, and (3) environmental/lifestyle. Conclusion: The results of this scoping review conclude that the inconsistency in research regarding brain fog in 20 chronic musculoskeletal pain is obstructing a clear understanding of the phenomenon and therefore may be impeding persons with chronic pain and brain fog from receiving optimal care.
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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 | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".