Quantifying the Degree of Fatigue in People Reporting Symptoms of Post-COVID-19 Syndrome: Results from a Rasch Analysis
Bibliographic record
Abstract
Purpose: Fatigue is a defining feature of post-COVID-19 syndrome (PCS), yet there is no accepted measure of this life-altering consequence. The aim here was to create a measure fit for the purposes of quantifying the severity of PCS fatigue and provide initial evidence for its relationships with measures of converging constructs. Method: A cross-sectional analysis of the first 414 participants in the Quebec Action for Post-COVID cohort study who self-identified with PCS was undertaken. In total, 17 items were available, including items commonly used in fatigue studies and to identify post-exertional malaise (PEM). Results: Rasch analysis identified that 10 of the 17 items fit a unidimensional linear model with a theoretical range from 0 to 21 (none to highest fatigue). The PCS Fatigue Severity Measure V1 (mean 13.8 [SD 4.7]) correlated highly with criterion measures of fatigue (r ≈│0.8│). Correlations with converging constructs of pain, physical function, and health rating exceeded │0.5│. Conclusions: PCS Fatigue Severity Measure V1 was distinguished between people working versus those on sick leave (difference: 5.1 points; effect size: 1.08). Effect sizes for people with and without irritability or meeting criteria for post-traumatic distress were approximately equal to 0.5. There is sufficient evidence that this measure is fit for purpose for quantifying fatigue in this population at one point in time. Further evidence in other samples is required to verify content and performance over time.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".