Are health care providers who work with cancer drugs at an increased risk for toxic events? A systematic review and meta-analysis of the literature
Bibliographic record
Abstract
OBJECTIVE: A systematic review and meta-analysis was conducted to test the hypothesis that oncology health care workers are at an increased risk of cancer, reproductive complications and acute toxic events. DESIGN: A structured literature search of Index Medicus/ MEDLINE, CINAHL, EMBASE, the Cochrane Database of Systematic Reviews and Healthstar was performed from 1966 to December 2004 for human epidemiological studies evaluating the risk of toxic events in health care workers exposed to cytotoxic drugs. Raw data and adjusted odds ratios (OR) reported in eligible studies were combined using a random effects model to calculate point estimates and 95% confidence intervals (CI) for each potential risk outcome. MAIN OUTCOME MEASURES: Adjusted OR for congenital malformations, stillbirths and spontaneous abortions among health care workers exposure to cytotoxic agents compared to a nonexposed control group. RESULTS: The systematic review identified 14 studies evaluating the outcomes of interest, seven of which were suitable for statistical pooling. Due to lack of evidence, we were unable to estimate a pooled OR for the risk of cancer and acute toxic events. However, no significant association was detected between exposure to cytotoxic drugs and; congenital malformations (OR = 1.64; 95% CI: 0.91-2.94) and stillbirths (OR = 1.16; 95% CI: 0.73-1.82). In contrast, an association was identified between exposure to chemotherapy and spontaneous abortions (OR = 1.46; 95% CI: 1.11-1.92). CONCLUSIONS: The results of this systematic review identified a small incremental risk for spontaneous abortions in female staff working with cytotoxic agents. Health policy decision makers should effectively communicate the magnitude of this risk to their staff and implement cost effective interventions for its reduction or elimination.
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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 | 0.027 | 0.091 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.022 | 0.043 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".