Repeated Hand Grip Strength is an Objective Marker for Disability and Severity of Key Symptoms in Post-COVID ME/CFS
Bibliographic record
Abstract
Abstract Post-COVID Syndrome (PCS) refers to a diverse array of symptoms that persist beyond 3 months of the acute phase of a SARS-CoV-2 infection. The most frequent symptom is fatigue, which can manifest both mentally and physically. In this study, handgrip strength (HGS) parameters were determined as an objective measure of muscle fatigue and fatigability. HGS parameters were correlated with other frequent symptoms among 144 female PCS patients suffering from fatigue, exertional intolerance, and cognitive impairment. Seventy-eight patients met the Canadian Consensus Criteria (CCC) for post-infectious myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The severity of disability and key symptoms were evaluated utilizing self-reported questionnaires. Notably, patients diagnosed with ME/CFS exhibited a higher overall severity of symptoms, including lower physical function ( p < 0.001), a greater degree of disability ( p < 0.001), more severe fatigue ( p < 0.001), post-exertional malaise ( p < 0.001), and autonomic dysfunction ( p = 0.004). While HGS was similarly impaired in both PCS and ME/CFS patients, the associations between HGS and the severity of symptoms and disability revealed striking differences. We observed significant correlations of HGS parameters with physical function across all patients, but with the key symptoms PEM, fatigue, cognitive impairment, and autonomic dysfunction in ME/CFS patients only. This points to a common mechanism for these symptoms in the ME/CFS subtype, distinct from that in other types of PCS. Further HGS provides an objective marker of disease severity in ME/CFS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".