Bibliographic record
Abstract
As we near the third year of the COVID-19 pandemic, greater attention is now being paid to the potential long-term consequences of SARS-CoV-2 in the hundreds of millions of people infected globally. A syndrome termed “long COVID” has emerged, which predominantly manifests as persistent fatigue, dyspnea, chest pain, and cognitive dysfunction following acute infection. The incidence of long COVID is in the range of 15% based on current best evidence, and symptoms are likely a result of several different pathophysiological mechanisms including multi-organ injury from acute infection, systemic viral persistence, immune dysregulation, and/or autoimmunity. Pulmonary symptoms represent a significant component of long COVID, and there is a growing body of research describing the epidemiology, risk factors, physiology, and radiology of the respiratory manifestations of long COVID. In this clinical review, we examine the most recent evidence relating to “respiratory long COVID,” discuss how innovative technologies such as Xenon-129 gas transfer magnetic resonance imaging (MRI) and respiratory oscillometry are helping to elucidate its unique pathophysiology, and consider the role of preventative strategies and possible treatments such as adapted pulmonary rehabilitation. The burden of respiratory long COVID is likely to continue to grow, and all healthcare professionals who care for patients with respiratory disease must prepare for this emerging chronic condition. This will require increased resources from healthcare decision makers, inventive approaches to healthcare delivery, further research, and the same spirit of collaboration that has enabled the many success stories to date in the global effort against COVID-19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".