Prevalence of Eye Strain Among Radiologists: Influence of Viewing Variables on Symptoms
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Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of and factors contributing to eye strain among radiologists, we examined the influence of the viewing method (PACS vs hard-copy film), age, case volume, technique, work habits, and workstation design on symptoms. MATERIALS AND METHODS: An Internet-based survey was sent to 2,700 radiologists randomly selected from the membership database of the Radiological Society of North America. Questions included demographic information, viewing method, work habits, and workstation design. Common eye strain symptoms were evaluated on a 5-point Likert scale. Chi-square analysis, analysis of variance, and step-wise and regression analyses were performed to evaluate codependence of the explanatory variables with eye strain. RESULTS: The adjusted response rate was 14% (380 respondents). The largest age cohort was 36-50 years. The prevalence of eye strain was 36% and was not affected by the viewing method (PACS vs film). Increased symptoms could be independently predicted in radiologists who were women (p <0.001), had longer work days (p=0.009), took fewer breaks (p=0.03), reported screen flicker (p=0.0003), and performed CT screening (p=0.04). Working hours had the strongest influence on eye strain. Eye strain was increased in those who reported studies for longer than 6 hr per day (p=0.01) and decreased in those who took breaks every hour (p=0.04). Symptoms were independent of the length of the break taken and of other workstation and technique factors. CONCLUSION: Eye strain was common among the radiologists in our study population, with no significant difference between PACS and hard-copy film users. Taking frequent short breaks, eliminating screen flicker, and limiting the number of CT screening studies interpreted may improve symptoms.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it