Post‐<scp>COVID</scp>‐19 Condition in Track and Field Master Athletes: Severity, Symptoms, and Associations With Quality of Life and C‐Reactive Protein Levels
Bibliographic record
Abstract
ABSTRACT Here, we assessed the prevalence of post‐COVID‐condition (PCC, also known as long‐COVID) and investigated its associations with health‐related quality of life and immune‐related biomarkers in track and field masters athletes (MAs). A total of 216 MAs (114 males, 102 females; age: 58.3 ± 11.9 vs. 56.6 ± 11.7 years; BMI: 23.6 [22.2–24.8] vs. 21.3 [20.0–23.6] kg/m2) reported their post‐COVID‐conditions via the Post‐COVID Syndrome Questionnaire (PCSQ). In a subgroup of 108 MAs, fasting blood samples were collected to assess C‐reactive protein (CRP) levels as a biomarker of immune status (MAs‐CRP). Based on their PCSQ sum score, MAs were divided into three groups: no/mild, moderate, and severe. Associations between PCC severity and sex, athletic specialty, and competition level were evaluated using Fisher's exact test. Forty‐six (21%) MAs were identified with clinically relevant moderate‐to‐severe post‐COVID‐19 conditions (PCSQ score > 10.75). The most frequently reported symptoms included musculoskeletal pain (15%), sleep disturbance (13%), sensory or respiratory symptoms (11%), fatigue (11%), and flu‐like symptoms (11%). PCC prevalence did not differ by sex, athletic specialties, training load, or prior competition level (all p > 0.05). MAs with moderate‐to‐severe PCC had significantly lower physical and mental component scores of quality of life compared with those with no or mild symptoms (p < 0.05). In the MAs‐CRP subgroup, self‐reported cardiac ailments and flu‐like symptoms were significantly and positively associated with CRP levels (Spearman ρ = 0.27–0.30, all p < 0.01). Post‐COVID‐19 condition is associated with reduced quality of life in track and field masters athletes, independent of sex, prior competition levels, and training characteristics. Furthermore, low‐grade inflammation based on CRP levels was associated with self‐reported cardiac and flu‐like symptoms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".