Change in Fatigue over 15 months in people the Post-Covid Syndrome
Bibliographic record
Abstract
Abstract Objective The objective was to estimate unique patterns of change in fatigue in people with the Post-Covid Syndrome (PCS) over 15 months. Design/Subjects The Quebec Action for Post-COVID (QAPC) study was a prospective study designed to provide a patient-centered understanding of symptoms, function, and quality of life in a self-identified Quebec sample. Methods Participants were queried every 3 months about symptoms and function. Fatigue was measured with the 10-item Post-COVID Syndrome Fatigue Severity Measure with a transformed score ranging from 0 no fatigue to 100 extreme fatigue. Group Based Trajectory Analysis (GBTA) was used to identify patterns of longitudinal change. Results 545 people had an average value of fatigue at baseline of 62.2 / 100 (SD: 21.6); 25% of the cohort had 5 visits. Six trajectories of individual change emerged: two groups with the highest fatigue showed persistence over time; a small group with high fatigue showed improvement; two groups with average fatigue showed improvement over time; and the group with low fatigue showed no emergence of this symptom, Conclusion High fatigue seems to persist over time while less severe fatigue abates. High fatigue may indicate a sub-syndrome within PCS similar to chronic fatigue syndrome. Lay Abstract The Post-Covid syndrome (PCS) affected people world-wide and many people still suffer its long term effects. Fatigue is a defining symptom of PCS and there is no evidence-based treatment for this life altering symptom. Using a PCS specific measure of fatigue severity developed using modern measurement theory, six different patterns of longitudinal change were observed. People with the most severe fatigue showed no recovery over time except for a very small group who did recover. People with moderate fatigue showed some improvement. The notion that severe fatigue could be a separate clinical entity needs further study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".