A Physiotherapy Framework to Managing Long COVID: A Clinical Approach
Bibliographic record
Abstract
Purpose: Individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19), can experience ongoing, often debilitating symptoms after the acute infection known as Long COVID (LC). LC has profound medical, social, and economic consequences worldwide. Prevalence estimates vary, but it is estimated that 10% to 35% of people infected with SARS-CoV-2 develop LC. The World Health Organization endorses physiotherapy as a vital component in LC symptom management and stabilization. Cardiorespiratory physiotherapists are often involved in the management of patients with LC phenotypes such as post-exertional malaise/post-exertional symptom exacerbation, post-COVID interstitial lung disease, dysautonomia, breathing pattern disorders, and chronic cough. However, specific guidance is lacking regarding physiotherapy assessment and safe intervention strategies. In this review, we describe the relevant pathophysiology of the condition, report common clinical phenotypes, and propose a clinical framework for physiotherapy assessment and safe intervention strategies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".