Fatigue Severity, Cognitive Strain, and Psychological Health in Long COVID: Untangling the Interconnected Aftermath from a Dedicated Long COVID Clinic
Bibliographic record
Abstract
Post-acute sequelae of SARS-CoV-2 infection (PASC) frequently includes persistent fatigue and cognitive dysfunction, but the relationship between these symptoms remains poorly defined. In this prospective observational study at the Henry Ford St. John Long COVID Clinic (LCC) from July 2023 to March 2025, we assessed fatigue severity using the Fatigue Assessment Scale (FAS) and examined its relationship with depression and cognitive symptoms. New patients completed demographic and clinical questionnaires, Patient Health Questionnaire (PHQ)-9, and Montreal Cognitive Assessment (MoCA) at their first LCC visit. Among 41 patients, 35 (85.4%) met the inclusion criteria for fatigue (FAS ≥ 22), with 18 (51.5%) experiencing severe fatigue (FAS > 34). Severe fatigue was significantly associated with shortness of breath, chest pain, and depression. Patients experiencing severe fatigue had significantly higher median PHQ-9 scores (12.5) compared to those with mild to moderate fatigue (5.0, p < 0.001). However, there were no significant differences in MoCA scores between these groups. Our study suggests a strong relationship between fatigue and depression in patients with PASC, emphasizing the importance of integrated physical and psychological healthcare. Moreover, since cognitive performance does not vary with fatigue levels, all PASC patients with cognitive dysfunction should receive routine cognitive screenings, regardless of the severity of their fatigue.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".