Work‐attributed symptom clusters (darkroom disease) among radiographers versus physiotherapists: Associations between self‐reported exposures and psychosocial stressors
Bibliographic record
Abstract
BACKGROUND: "Darkroom disease" (DRD) has been used to describe unexplained multiple symptoms attributed by radiographers to their work environment. This study determines the prevalence of symptom clusters similar to other unexplained syndromes among (medical radiation technologists (MRTs) as compared with physiotherapists (PTs), and identifies associated work-related (WR) factors. METHODS: A mail survey was undertaken of members of the professional associations of MRTs and PTs in Ontario, Canada. Questions were included to determine the prevalence and frequency of symptom clusters including abnormal tiredness as well as WR headaches, and symptoms suggestive of eye, nasal, and throat irritation. For the purpose of this study, these are considered to be DRD symptom clusters. Individuals with doctor-diagnosed asthma were excluded from our analyses. RESULTS: Overall, 63.9% of MRTs and 63.1% of PTs participated. Criteria for DRD were met by 7.8% of 1,483 MRTs and 1.8% of 1,545 PTs [odds ratio, OR 4.8 (confidence interval, CI 3.1-7.5); (P < 0.0001)]. Both occupations showed significant associations between responses reflecting psychosocial stressors and DRD. Those with this symptom cluster were more likely to report additional symptoms than those without, and MRTs with DRD symptoms reported significantly more workplace chemical exposures. CONCLUSIONS: Findings suggest excess symptoms consistent with DRD among MRTs versus PTs, and there were associations among those meeting our definition of DRD with self-reported irritant exposures and psychosocial stressors.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".