The Multisystem effects of Long COVID Syndrome and Potential Benefits of Massage Therapy in Long COVID Care
Bibliographic record
Abstract
Background: A major complication of infection with Severe Acute Respiratory Coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, is the potential for Long COVID Syndrome. While the pathophysiology of Long COVID Syndrome has yet to be described, the disease presentation is characterized by long-term symptoms with debilitating effects on human health. A better understanding of Long COVID symptomology may open up new avenues for patient treatment such as massage therapy. Methods: From the PubMed database, cohort studies that examined post-infection COVID sequelae published between January 1st, 2021 and April 30th, 2021 were selected to investigate patient demographics and symptoms. A review of massage therapy literature since 2000 in conjunction with identified Long COVID symptoms was performed. Results: This systematic review identified 17 cohort studies across the world that investigated the symptomatology of patients suffering from post-COVID sequelae in multiple organ systems. We identified the pulmonary and nervous systems to be the organ systems most affected with post-COVID sequelae, with PTSD, fatigue, dyspnea, cough, sleep disturbances, loss of smell, abdominal pain, and decreased appetite as the most common symptoms reported by >20% of Long COVID patients. Massage therapy was historically found to provide benefits to patients experiencing similar symptoms to those identified in Long COVID. Conclusions: Recognizing the need for new approaches to treatment for Long COVID Syndrome, we identify massage therapy as a potential therapeutic treatment to positively impact the organ systems affected by Long COVID, especially the high-incident symptoms, and improve patient quality of life.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".