Deoxygenation Trends and Their Multivariate Association with Self-Reported Fatigue in Post-COVID Syndrome
Bibliographic record
Abstract
Background/Objectives: A relevant subgroup of post-COVID-19 syndrome (PCS) patients suffers from post-exertional malaise (PEM) and cardiovascular or neurological symptoms, impairing daily functioning up to becoming even house- or bedbound. Recent data suggest that PCS summarizes different subgroups, one of them being characterized by an impaired microcirculation. Thus, the aim of the present study was to investigate local deoxygenation, measured with non-invasive near-infrared regional spectroscopy (NIRS), and its association with self-reported fatigue in patients with PCS compared to controls in light exercise. Methods: 150 participants (100 PCS patients and 50 controls) were recruited. PEM was assessed using FACIT, Chalder, and Bell scoring and Canadian Criteria. NIRS was used to measure local oxygenation while kneading a stress ball and during recovery. Results: PCS patients showed fatigue scores of 30 (Bell score), 20.6 (FACIT fatigue score), and 9.914 (Chalder fatigue score). Decreased deoxygenation peaks at the start of exercise were observed in patients with PCS, compared to controls (p = 0.0002). Multivariate analysis identified a subgroup, showing an association between strong fatigue and restricted oxygenation dynamics. Conclusions: NIRS could be a potential tool to assess deoxygenation deficits even in moderate to severely impaired PCS patients using light exercise protocols.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".