Reducing fatigue-related symptoms in Long COVID-19: a preliminary report of a lymphatic drainage intervention
Bibliographic record
Abstract
In the early days of the first global wave of the COVID-19 pandemic, the potential for a postviral syndrome to manifest following COVID-19 infection was first recognized. Here, we present an analysis of a case series of the first 20 patients' data collected in clinical practice to evaluate the potential of a possible alternative treatment for Long COVID. Methods: Face-to-face treatment sessions with Perrin technique practitioners occurred weekly involving effleurage/other manual articulatory techniques. The individuals being treated also undertook daily self-massage along with gentle mobility exercises. Patients recorded symptom severity using the self-report 54-item profile of fatigue-related states (PFRS) before and after treatment. Results: The mean age of male patients was 41.8 years (range, 29-53 years), and for female patients, 39.3 years (range, 28-50 years). None of the participants had a prior diagnosis of chronic fatigue syndrome, and all were new attendees to the clinics at the time of initial assessment. The average number of treatment sessions was 9.7 in men and 9.4 in women. The reduction in PFRS scores was 45% in men and 52% in women. The highest subscale scores on average were for fatigue, with the lowest for somatic symptoms. All subscale scores showed, on average, a similar reduction of approximately 50% postintervention, with the reduction in score relating to a decrease in the severity of symptoms. Conclusion: Our findings suggest that a specific manual lymphatic drainage intervention may help to reduce fatigue symptoms related to Long COVID. Perhaps preventing acute symptoms through early intervention.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".