Positive Effects of Cognitive-Behavioral Therapy Targeting Severe Fatigue Following COVID-19 Are Sustained Up to 1 Year After Treatment
Bibliographic record
Abstract
To the Editor—Recently, our article entitled “Efficacy of Cognitive-Behavioral Therapy Targeting Severe Fatigue Following Coronavirus Disease 2019: Results of a Randomized Controlled Trial” [1] was published in Clinical Infectious Diseases. This study demonstrated a beneficial effect of cognitive-behavioral therapy (CBT) in reducing severe fatigue following coronavirus disease 2019 (COVID-19), as compared with care as usual. All secondary outcomes also favored CBT. Positive effects were maintained up to 6 months post-treatment [1]. In this letter, we present the 1 year follow-up outcomes of CBT for post–COVID-19 fatigue. All details on the methods used in this follow-up study are described in the published study protocol [2] and the Supplementary Appendix. In this long-term follow-up study, all 57 patients randomized to CBT were eligible. Of them, 52 participated. For ethical reasons, patients randomized to care as usual were offered CBT and could therefore no longer serve as a control. The primary outcome was fatigue severity. Secondary outcomes were physical functioning, problems with social functioning, somatic symptom severity, problems concentrating, and proportions of patients being no longer severely fatigued, no longer severely fatigued with a reliable change, and not chronically fatigued. Additionally, for each individual patient, it was calculated whether the change in fatigue severity between 6 months and 1 year post-CBT was reliable and/or clinically significant.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".