Is Long COVID Syndrome a Transient Mitochondriopathy Newly Discovered: Implications of CPET
Bibliographic record
Abstract
The new outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has an impact worldwide, primarily as a medical emergency. Even that the total constellation is considered as a pandemic of acute respiratory disease, coronavirus disease 2019 (COVID-19) gives rise to dysfunction in multiple organs (e.g., brain, lungs, heart, muscles) that impairs cardiopulmonary (CP) function. Parallel to the CP consequences of COVID-19 is a significant reduction in physical activity. Cardiopulmonary exercise testing (CPET) is daily used in clinical practice to define prognosis, provide risk stratification and treatment strategy. As such, the significance of CPET is crucial concerning clinical assessments of COVID-19 patients. Furthermore, new studies aim at understanding the effects of SARS-CoV-2 infection in long term. Multiple studies have investigated the cardiopulmonary function and impairment of exercise endurance in such patients, as well as a possible prolonged physical impairment. With this review, we summarize the COVID-19-associated pathophysiology for the Long COVID (LC) syndrome as well as the importance of performing CPET.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".