Multidimensional Characterization of Long COVID Fatigue
Bibliographic record
Abstract
OBJECTIVES: We performed a multidimensional analysis of mood, cognition, sleep and circadian rhythms in patients with post-acute sequelae of SARS-CoV-2 infection (PASC) with the objective of characterizing the phenotype of PASC fatigue. METHODS: We recruited adult patients from a Neuro-COVID-19 Clinic with persistence of disabling symptoms beyond 6 weeks from acute infection. Self-reported symptoms were assessed with Patient-Reported Outcomes Measurement Information System instruments. We evaluated cognitive performance using NIH Toolbox measures and assessed sleep and rest-activity rhythms by 7 days of wrist actigraphy. We performed level 2 polysomnography in a subset of 20 participants. RESULTS: We studied 58 participants: 83% White, 59% female and 91% not hospitalized for COVID-19. Fatigue severity was significantly correlated with worse self-reported cognitive abilities but not with objectively measured cognitive performance and with greater depression symptoms, several rest-activity rhythm and light exposure disruption measures, and greater actigraphy measured sleep time and time in bed. A multivariable model found significant, independent associations between fatigue severity and subjective cognitive abilities, depression symptoms, and rest-activity rhythm disruption. CONCLUSIONS: Long total sleep times, disruption of light exposure and circadian rest-activity patterns, depression and subjective cognitive impairment are associated with PASC fatigue. Behaviorally influenced sleep and circadian abnormalities may exacerbate fatigue and be targets for therapeutic interventions.
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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 | 0.000 |
| 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) | 0.001 | 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 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".