Prevalence of unjustified emergency department x-ray examination referrals performed in a regional Queensland hospital: a pilot study

Introduction: The underpinning principles of radiation protection are justification, optimisation and limitation. Each medical imaging referral that uses ionising radiation must balance the justification of exposure to radiation against the benefits of the examination. Scrutiny of justification is the role of radiographers, for general radiography, and is usually performed using the clinical details provided on the referral. International studies report up to 77% of medical imaging examinations are unjustified or inappropriate. In regional Queensland, justification seems to involve a subjective assessment and enforcement is ad hoc. This study aimed to determine the number of unjustified emergency department x-ray examinations performed in a regional Queensland hospital. Methods: An audit of the clinical details provided on x-ray referrals and in the medical records was performed on x-ray examinations undertaken within an 11-day period. Justification was determined by compliance with the Government of Western Australia's diagnostic imaging pathways. Results: Of the 186 referrals assessed, 75.3% were categorised as not having complied with the imaging pathway and were considered unjustified. When the clinical details in the patient's medical record were reviewed, in conjunction with the referral, the unjustified rate reduced to 49.2% of examinations. Conclusion: Results demonstrate a lack of information transfer by referring clinicians and a lack of compliance with justification requirements for imaging by medical imaging staff. Improved communication regarding the need for imaging, and the refusal of referrals that are not justified, will ensure that patients are only exposed to radiation when clear benefit has been demonstrated

Plutonium from Above-Ground Nuclear Tests in Milk Teeth: Investigation of Placental Transfer in Children Born between 1951 and 1995 in Switzerland

BACKGROUND: Occupational risks, the present nuclear threat, and the potential danger associated with nuclear power have raised concerns regarding the metabolism of plutonium in pregnant women. OBJECTIVE: We measured plutonium levels in the milk teeth of children born between 1951 and 1995 to assess the potential risk that plutonium incorporated by pregnant women might pose to the radiosensitive tissues of the fetus through placenta transfer. METHODS: We used milk teeth, whose enamel is formed during pregnancy, to investigate the transfer of plutonium from the mother's blood plasma to the fetus. We measured plutonium using sensitive sector field inductively coupled plasma mass spectrometry techniques. We compared our results with those of a previous study on strontium-90 ((90)Sr) released into the atmosphere after nuclear bomb tests. RESULTS: Results show that plutonium activity peaks in the milk teeth of children born about 10 years before the highest recorded levels of plutonium fallout. By contrast, (90)Sr, which is known to cross the placenta barrier, manifests differently in milk teeth, in accordance with (90)Sr fallout deposition as a function of time. CONCLUSIONS: These findings demonstrate that plutonium found in milk teeth is caused by fallout that was inhaled around the time the milk teeth were shed and not from any accumulation during pregnancy through placenta transfer. Thus, plutonium may not represent a radiologic risk for the radiosensitive tissues of the fetus

Moving radiation protection on from the limitations of empirical concentration ratios

Radionuclide activity concentrations in food crops and wildlife are most often predicted using empirical concentration ratios (CRs). The CR approach is simple to apply and some data exist with which to parameterise models. However, the parameter is highly variable leading to considerable uncertainty in predictions. Furthermore, for both crops and wildlife we have no, or few, data for many radionuclides and realistically, we are never going to have specific data for every radionuclide - wildlife/crop combination. In this paper, we present an alternative approach using residual maximum likelihood (REML) fitting of a linear mixed effects model; the model output is an estimate of the rank-order of relative values. This methodology gives a less uncertain approach than the CR approach, as it takes into account the effect of site; it also gives a scientifically based extrapolation approach. We demonstrate the approach using the examples of Cs for plants and Pb for terrestrial wildlife. This is the first published application of the REML approach to terrestrial wildlife (previous applications being limited to the consideration of plants). The model presented gives reasonable predictions for a blind test dataset

Potential for Inhalation Exposure to Engineered Nanoparticles from Nanotechnology-Based Cosmetic Powders

Background: The market of nanotechnology-based consumer products is rapidly expanding, and the lack of scientific evidence describing the accompanying exposure and health risks stalls the discussion regarding its guidance and regulation

Informing Selection of Nanomaterial Concentrations for ToxCast in Vitro Testing Based on Occupational Exposure Potential

Background: Little justification is generally provided for selection of in vitro assay testing concentrations for engineered nanomaterials (ENMs). Selection of concentration levels for hazard evaluation based on real-world exposure scenarios is desirable

Mathematical methods and models for radiation carcinogenesis studies

Research on radiation carcinogenesis requires a twofold approach. Studies of primary molecular lesions and subsequent cytogenetic changes are essential, but they cannot at present provide numerical estimates of the risk of small doses of ionizing radiations. Such estimates require extrapolations from dose, time, and age dependences of tumor rates observed in animal studies and epidemiological investigations, and they necessitate the use of statistical methods that correct for competing risks. A brief survey is given of the historical roots of such methods, of the basic concepts and quantities which are required, and of the maximum likelihood estimates which can be derived for right censored and double censored data. Non-parametric and parametric models for the analysis of tumor rates and their time and dose dependences are explained