A prospective-controlled study of pregnant veterinary staff exposed to inhaled anesthetics and x-rays.
Bibliographic record
Abstract
Most veterinary staff are women of reproductive age. They are exposed to "waste" anesthetic gas and ionizing radiation in their workplace, which may endanger fetal safety. Presently, exposure of female veterinary staff to these health hazards has not been adequately addressed in the medical literature. Our primary objective was to investigate the incidence of major malformations associated with occupational exposure to inhaled anesthetics and/or radiation among pregnant veterinary staff. The secondary objective was to determine the rates of other adverse outcomes. We prospectively collected data on and followed-up women occupationally exposed to inhaled anesthetics and/or radiation in veterinary practices in Ontario, and compared them to controls matched for maternal age and gestational age at the time of call to the Motherisk Program. A total of 95 women were prospectively enrolled and followed-up. Among the participants there were 87 (93.5%) and 88 (92.8%) livebirths in the study and control groups, respectively. There were 4 (4.8%) major birth defects in the study group and 3 (3.4%) in the control group. The rates of spontaneous abortion were also similar, 6 (6.4%) cases in the study group and 7 (7.4%) cases in the control group. These results suggest that Ontario female veterinary staff exposed to inhaled anesthetics and/or radiation do not seem to be at an increased risk for major malformations above baseline risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".