Quantifying The Symptom Burden Of Acute Exercise In Gulf War Illness
Bibliographic record
Abstract
Post-exertional malaise is described as symptom worsening following physical or mental exertion and is considered a characteristic of Gulf War Illness (GWI). Experimentally, this phenomenon has been difficult to quantify due to high variability in symptom profiles across Veterans with GWI. Hence, we developed a metric to better represent post-exercise symptom responses. PURPOSE: To test an integral-based method that captures three key components: (1) symptom severity, (2) symptom time course, and (3) symptom changes pre- to post-exercise. METHODS: Veterans with GWI (N = 35) and healthy Veteran controls (N = 28) performed 30 minutes of cycling exercise (70% ± 5% heart rate reserve). Symptoms were measured for three days pre- and post-exercise using 0-100 visual analogue scales (VAS) operationalized from the GWI Kansas case definition. An area under the curve (AUC) was calculated using mathematical integrals of VAS responses for pre- and post-exercise periods. Symptom scores for fatigue, neurocognitive/mood, and pain domains were calculated as a ratio of the change in AUC from pre- to post-exercise multiplied by the post-exercise AUC. Spearman correlations were calculated between symptom scores and validated measures of fatigue (Brief Fatigue Inventory; BFI), mood (Profile of Mood States; POMS), and pain (Short-Form McGill Pain Questionnaire 2; SF-MPQ-2). RESULTS: Fatigue-related symptom scores were significantly (p < 0.05) correlated with BFI scores at 24, 48 and 72 hours post-exercise (rs = 0.67 - 0.73) as well as POMS fatigue subscale scores 24 hours post-exercise (rs = 0.58). Neurocognitive/mood-related symptom scores were significantly correlated with 24 hour post-exercise POMS total mood disturbance (rs = 0.5). Pain-related symptom scores were significantly correlated with total SF-MPQ-2 scores at 24 hours post-exercise (rs = 0.54). CONCLUSIONS:The proposed symptom score is a suitable representation of the symptom burden experienced by Veterans with GWI following acute exercise. Future research will aim to further validate this measure and explore associations with biological outcomes. Supported by a grant from the U.S. Department of Veterans Affairs #I01 CX001329 Supported by a grant from the U.S. Department of Veterans Affairs #I01 CX001329
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".