Prevalence of Eye Strain Among Radiologists: Influence of Viewing Variables on Symptoms
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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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 | 0.009 |
| 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 | 0.000 | 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".