Subjective Dry Eye Symptoms and Objective Ocular Surface Signs in a Civil Air Crew Population: A Cross-Sectional Study
Bibliographic record
Abstract
Background Aviation professionals are often exposed to conditions such as low cabin air pressure, reduced humidity, and prolonged artificial lighting, which may predispose them to dry eye disease (DED). We therefore designed a cross-sectional study with three primary objectives. Our first aim was to determine the prevalence of subjective dry eye symptoms among civil flight personnel. To achieve this, we administered the Standard Patient Evaluation of Eye Dryness (SPEED) questionnaire. Second, we performed ocular examinations to assess objective DED indicators, such as the Schirmer's-1 (SCH-1) test and tear film break-up time (TBUT). We then correlated the results of these objective tests with the subjective symptoms reported by the participants. Last, we aimed to identify the independent risk factors for positive SCH-1 and TBUT results among aircrew personnel who did not report subjective dry eye symptoms. Methods The study sample consisted of 189 aircrew personnel (94 men and 95 women; mean age: 35.8 ± 10.4 years). They completed the SPEED questionnaire, a tool for assessing ocular surface symptoms. Participants' symptoms were classified as normal (SPEED scores 0-6), moderate (SPEED scores 7-15), or severe (SPEED scores 16-28). The objective assessment included the SCH-1 test for tear production and the TBUT test for tear film quality. Results A significant majority of participants (n = 165; 87.3%) did not report any subjective symptoms of DED. However, 12.2% (n = 23) and 0.5% (n = 1) of the study subjects experienced moderate and severe symptoms, respectively. The SCH-1 test and TBUT test were positive in 25.4% (n = 48) and 24.9% (n = 47) of the participants, respectively. Interestingly, among the aircrew personnel who did not report any subjective dry eye symptoms (SPEED scores 0-6), 18.8% (n = 31) and 17.6% (n = 29) showed abnormal results on the SCH-1 test and TBUT, respectively. Age was identified as the only independent predictor of a positive TBUT (odds ratio = 1.05, 95% confidence interval (CI) = 1.01-1.08, p = 0.01), with a borderline significant association with a positive SCH-1 test (odds ratio = 1.03, 95% CI = 0.99-1.07, p = 0.06). Conclusions The disparity between subjective symptoms and objective tests emphasizes the significance of incorporating objective measures for screening and diagnosing DED in civil flight personnel. If independently confirmed by future research, our findings could potentially lead to the routine implementation of surveillance protocols that incorporate objective DED indicators. Moreover, as age emerged as an independent predictor of positive results on objective tests, it is crucial to consider age-specific screening strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".