Dry eye symptoms and signs in United States Gulf War era veterans with myalgic encephalomyelitis/chronic fatigue syndrome
Bibliographic record
Abstract
BACKGROUND: To examine ocular symptoms and signs of veterans with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) diagnosis, ME/CFS symptoms, and controls. METHODS: This was a prospective, cross-sectional study of 124 South Florida veterans in active duty during the Gulf War era. Participants were recruited at an ophthalmology clinic at the Miami Veterans Affairs Hospital and evaluated for a diagnosis of ME/CFS, or symptoms of ME/CFS (intermediate fatigue, IF) using the Canadian Consensus criteria. Ocular symptoms were assessed via standardised questionnaires and signs via comprehensive slit lamp examination. Inflammatory blood markers were analysed and compared across groups. RESULTS: Mean age was 55.1 ± 4.7 years, 88.7% identified as male, 58.1% as White, and 39.5% as Hispanic. Ocular symptoms were more severe in the ME/CFS (n = 32) and IF (n = 48) groups compared to controls (n = 44) across dry eye (DE; Ocular Surface Disease Index [OSDI]: 48.9 ± 22.3 vs. 38.8 ± 23.3 vs. 19.1 ± 17.8, p < 0.001; 5 item Dry Eye Questionnaire [DEQ-5]: 10.8 ± 3.9 vs. 10.0 ± 4.6 vs. 6.6 ± 4.2, p < 0.001) and pain-specific questionnaires (Numerical Rating Scale 1-10 [NRS] right now: 2.4 ± 2.8 vs. 2.4 ± 2.9 vs 0.9 ± 1.5; p = 0.007; Neuropathic Pain Symptom Inventory modified for the Eye [NPSI-E]: 23.0 ± 18.6 vs. 19.8 ± 19.1 vs. 6.5 ± 9.0, p < 0.001). Ocular surface parameters and blood markers of inflammation were generally similar across groups. CONCLUSION: Individuals with ME/CFS report increased ocular pain but similar DE signs, suggesting that mechanisms beyond the ocular surface contribute to 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 | 0.000 | 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".