Surveillance urinaire des professionnels de la santé exposés aux antinéoplasiques dans le cadre de leur travail : revue de la littérature de 2010 à 2015
Bibliographic record
Abstract
RÉSUMÉ Contexte : Il existe de plus en plus de données sur la présence de traces de médicaments dangereux dans l’urine des professionnels de la santé exposés à ces médicaments. Objectif : Présenter une revue de la littérature scientifique concernant la surveillance urinaire de professionnels de la santé exposés aux anti - néoplasiques dans le cadre de leur travail. Sources de données : Recherche sur PubMed avec les Medical Subject Headings (MeSH) « occupational exposure » et « antineoplastic agents » ainsi que sur Google Scholar avec les termes « antineoplastic », « urine » et « occupational exposure ». Sélection des études et extraction des données : L’examen a porté sur tous les articles en anglais et en français ayant trait aux professionnels de la santé exposés à des médicaments dangereux dans le cadre de leur travail, publiés entre le 1er janvier 2010 et le 31 décembre 2015. Les articles ne comportant pas de résultats urinaires et ceux concernant les vétérinaires ainsi que les revues de littérature, les éditoriaux, les lettres à la redaction et les résumés de congrès ont été exclus. Synthèse des données : Vingt-quatre articles ont été retenus. Les études ont été menées dans 52 établissements de santé provenant de sept pays. Elles regroupaient 826 travailleurs exposés à des médicaments dangereux et 175 témoins, notamment des infirmiers (n = 16 études), des pharmaciens (n = 10), des assistants techniques en pharmacie (n = 8), des médecins (n = 7), des aides-soignants (n = 2) et autres (n = 8). Différentes méthodes analytiques ont été utilisées pour quantifier la présence de 13 médicaments dangereux, principalement le cyclophosphamide (n = 16 études), les platines (n = 7) et l’alpha-fluoro-béta-alanine, un métabolite urinaire du 5-fluorouracile (n = 3). La proportion de travailleurs qui ont étés déclarés positifs s’étendait de 0 % (n = 10 études) à 100 % (n = 4). Si l’on ne retient que les études permettant de calculer le taux de travailleurs comportant au moins un prélèvement urinaire positif (n = 23), la proportion totale était de 21 % (173/809 travailleurs, toutes méthodes et tous médicaments confondus). Conclusion : Vingt-quatre études de surveillance urinaire ont été réalisées au sein de sept pays entre 2010 et 2015. Dans plusieurs études, aucune trace de médicaments n’a été mesurée dans l’urine. ABSTRACT Background: There is increasing evidence that traces of hazardous drugs occur in the urine of health care professionals who are exposed to these drugs. Objective: To review the scientific literature regarding urinary monitoring of health care professionals exposed to antineoplastic drugs through their work. Data Sources: A search of PubMed using the Medical Subject Headings “occupational exposure” and “antineoplastic agents” and of Google Scholar using the terms “antineoplastic”, “urine”, and “occupational exposure”. Study Selection and Data Extraction: The analysis covered all articles in English or French pertaining to health care professionals exposed to hazardous drugs in the workplace, published from January 1, 2010, to December 31, 2015. Articles that did not discuss the results of urine tests and those concerning veterinarians, as well as literature reviews, editorials, letters to the editor, and conference abstracts, were excluded. Data Synthesis: Twenty-four articles were retained. The studies were conducted in 52 health care institutions in 7 countries. They included 826 workers exposed to hazardous drugs and 175 controls, specifically nurses (n = 16 studies), pharmacists (n = 10), pharmacy technicians (n = 8), physicians (n = 7), health care aides (n = 2), and others (n = 8). Various analytical methods were used to quantify the presence of 13 hazardous drugs, primarily yclophosphamide (n = 16 studies), platinum based drugs (n = 7), and alpha-fluoro-beta-alanine, a urine metabolite derived from 5-fluorouracil (n = 3). The proportion of workers with positive results ranged from 0% (n = 10 studies) to 100% (n = 4). Considering only those studies that allowed calculation of the rate of workers with at least one positive urine sample (n = 23), the total proportion was 21% (173/809 workers, for all methods and drugs combined). Conclusion: Twenty-four studies on urine monitoring were conducted in 7 countries between 2010 and 2015. In several studies, no traces of drugs were detected in urine.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".