Excess of symptoms among radiographers
Bibliographic record
Abstract
BACKGROUND: The prevalence and occupational determinants of miscellaneous symptoms referred to as "Darkroom disease" was compared between radiographers and physiotherapists working in British Columbia, Canada. METHODS: The participation rate for the mailout questionnaire survey was 66.1%. A small subset underwent spirometry and methacholine challenge testing. RESULTS: Radiographers had a higher prevalence of most symptoms, with an extreme odds ratio of 11.4 for chemical/metallic taste. The percentage of radiographers with non-specific bronchial hyperresponsiveness (NSBHR) was 3 times higher than that of physiotherapists, although the comparison was not statistically significant. Reporting inadequate ventilation, frequently detecting the odor of X-ray processing chemicals and cleaning up spills within the past 12 months were highly associated with most of the symptoms. CONCLUSIONS: Our results suggest that differences in the prevalence of symptoms represent a complex process, both in exposure and response to the many constituents found in radiographic processing chemicals. Objective testing of health outcomes and more refined exposure measurements are recommended to further investigate occupational health problems of radiographers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".