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Objectives: To estimate detection measures for tomosynthesis and standard mammography; to assess the feasibility of using tomosynthesis in population-based screening for breast cancer.Design, Setting: Prospective pilot trial comparing tomosynthesis (with synthesised 2D images) and standard mammography screening of women attending Maroondah BreastScreen, a BreastScreen Victoria service in the eastern suburbs of Melbourne.Participants: Women at least 40 years of age who presented for routine breast screening between 18 August 2017 and 8 November 2018.Main Outcome Measures: Cancer detection rate (CDR); proportion of screens that led to recall for further assessment.Results: 5018 tomosynthesis and 5166 standard mammography screens were undertaken in 10 146 women; 508 women (5.0% of screens) opted not to undergo tomosynthesis screening. With tomosynthesis, 49 cancers (40 invasive, 9 in situ) were detected (CDR, 9.8 [95% CI, 7.2-13] per 1000 screens); with standard mammography, 34 cancers (30 invasive, 4 in situ) were detected (CDR, 6.6 [95% CI, 4.6-9.2] per 1000 screens). The estimated difference in CDR was 3.2 more detections (95% CI, -0.32 to 6.8) per 1000 screens with tomosynthesis; the difference was greater for repeat screens and for women aged 60 years or more. The recall rate was greater for tomosynthesis (4.2%; 95% CI, 3.6-4.8%) than standard mammography (3.0%; 95% CI, 2.6-3.5%; estimated difference, 1.2%; 95% CI, 0.46-1.9%). The median screen reading time for tomosynthesis was 67 seconds (interquartile range [IQR] 46-105 seconds); for standard mammography, 16 seconds (IQR, 10-29 seconds).Conclusions: Breast cancer detection, recall for assessment, and screen reading time were each higher for tomosynthesis than for standard mammography. Our preliminary findings could form the basis of a large scale comparative evaluation of tomosynthesis and standard mammography for breast screening in Australia.Trial Registration: Australian New Zealand Clinical Trials Registry, ACTRN12617000947303. [ABSTRACT FROM AUTHOR]

Background: There is substantial variability in the prognosis of acute low back pain (LBP). The ability to identify the probability of individual patients recovering by key time points would be valuable in making informed decisions about the amount and type of treatment to provide. Predicting recovery based on presentation 1-week after initially seeking care is clinically important and may be more accurate than predictions made at initial presentation. The aim of this study was to predict the probability of recovery at 1-week, 1-month and 3-months after 1-week review in patients who still have LBP 1-week after initially seeking care.Methods: The study sample comprised 1070 patients with acute LBP, with a pain score of ≥2 1-week after initially seeking care. The primary outcome measure was days to recovery from pain. Ten potential prognostic factors were considered for inclusion in a multivariable Cox regression model.Results: The final model included duration of current episode, number of previous episodes, depressive symptoms, intensity of pain at 1-week, and change in pain over the first week after seeking care. Depending on values of the predictor variables, the probability of recovery at 1-week, 1-month and 3-months after 1-week review ranged from 4% to 59%, 19% to 91% and 30% to 97%, respectively. The model had good discrimination (C = 0.758) and calibration.Conclusions: This study found that a model based on five easily collected variables could predict the probability of recovery at key time points in people who still have LBP 1-week after seeking care.Significance: A clinical prediction model based on five easily collected variables was able to predict the likelihood of recovery from an episode of acute LBP at three key time points. The model had good discrimination (C = 0.758) and calibration. [ABSTRACT FROM AUTHOR]

Background Few studies have focused on the prevalence of obesity and hypertension among young people (ages 15-24). Aim To characterise the prevalence of obesity and systolic hypertension in young people aged 15-24 years across Australia. Methods Using data from the 2011-2012 Australian Health Survey, a national cross-sectional population-based survey, we included 2163 young people aged 15-24 years. Risk factors were estimated using multinomial logistic regression. Results The prevalence of obesity increased from 8% to 15% through the ages of 15-24 among males, but the prevalence of overweight and obesity were both 14% for females across all age groups. Low levels of physical activity were a strong risk factor for obesity for both males (odds ratio (OR) 5.95, 95% confidence intervals ( CI)1.83-19.36) and females ( OR 3.20 95% CI 0.69-14.87). Low socioeconomic status was associated with obesity among females only (first quintile OR 4.65, 95% CI 1.97-10.99). Although the prevalence of hypertension was low (4% males, 3% females), the prevalence of high normal blood pressure was substantial, especially among males (28% males, 14% females). Conclusions Overweight, obesity and high normal blood pressure were highly prevalent among Australian young people. Low levels of physical activity were identified as a risk factor for obesity for both male and females. Programmes targeting physical activity participation may need to be tailored differently for males and females, with a focus on females during early adolescence but early adult life for males. [ABSTRACT FROM AUTHOR]

Outcomes in clinical trials may be affected by the choice of treatment that participants might make, if they were indeed allowed to choose (a so-called selection effect), and by whether they actually receive their preferred treatment (a preference effect). Selection and preference effects can be important, but they cannot be estimated in the conventional trial design. An alternative approach is the two-stage randomised trial, in which participants are first randomly divided into two subgroups. In one subgroup, participants are randomly assigned to treatments, while in the other, participants are allowed to choose their own treatment. This approach yields estimates of the direct treatment effect, and of the preference and selection effects. The latter two provide insight that goes considerably beyond what is possible in the standard randomised trial. In this paper, we determine the optimal proportion of participants who should be allocated to the choice subgroup. The precision of the estimated selection, preference and treatment effects are functions of: the total sample size; the proportion of participants allocated to choose their treatment; the variances of the outcome; the proportions of participants who select each treatment in the choice group; and the selection, preference and treatment effects themselves. We develop general expressions for the optimum proportion of participants in the choice group, depending on which effects are of primary interest. We illustrate the results with trial data comparing alternative clinical management strategies for women with abnormal results on cervical screening. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

When no gold standard is available to evaluate a diagnostic or screening test, as is often the case, an imperfect reference standard test must be used instead. Furthermore, the errors of the test and its reference standard may not be independent. Some authors have opined that positively dependent errors will lead to overestimation of test performance. Although positive dependence does increase agreement between the test and the reference standard, it is not clear if test accuracy will necessarily be overestimated in this situation, and the case of negatively associated test errors is even less clear. To examine this issue in more detail, we derive the apparent sensitivity, specificity, and overall accuracy of a test relative to an imperfect reference standard and the bias in these parameters. We demonstrate that either positive or negative bias can occur if the reference standard is imperfect. The type and magnitude of bias depend on several components: the disease prevalence, the true test sensitivity and specificity, the covariance between the false-negative test errors among the true disease cases, and the covariance between the false-positive test errors among the true noncases. If, for example, sensitivity and specificity are 0.8 for both the test and reference standard and the errors have a moderate positive dependence, test sensitivity is then underestimated at low prevalence but overestimated at high prevalence, while the opposite occurs for specificity. We illustrate these ideas through general numerical calculations and an empirical example of screening for breast cancer with magnetic resonance imaging and mammography. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Aliment Pharmacol Ther 2010; 32: 270–281 Background Many bodies advise that people with bowel symptoms undergo colonoscopy to detect colorectal cancer. Aim To determine which bowel symptoms predict cancer on colonoscopy. Methods Information was collected on symptoms, demographics and medical history from patients subsequently undergoing colonoscopy. Multiple logistic regression modelling was used to identify predictors of colorectal cancer. An ROC curve was estimated for each model, and the area under the curve ( AUC) was computed. Results Cancer was found in 159 patients and no cancer or adenoma in 7577 patients. Bowel symptoms that predicted cancer were rectal bleeding, change in bowel habit and rectal mucus. Prediction was the strongest in patients who had symptoms at least weekly and commencing within the previous 12 months; abdominal pain was predictive only in such patients. The odds ratios never exceeded 4.27. A model based on age, gender, and medical history was highly predictive ( AUC = 0.79). Adding symptoms to this model increased the AUC to 0.85. Conclusions This model predicts patients in whom colonoscopy will have the highest yield. Conversely, colonoscopy can be avoided in people at low risk: in our study, 95% of cancers could have been detected by doing only 60% of the colonoscopies. [ABSTRACT FROM AUTHOR]

- Several studies of patients with prostatic carcinoma have reported worse survival rates for younger patients (<60 years) than for older and have concluded that this disease takes a more aggressive form in younger patients. We have retrospectively analysed data on 597 patients in East Anglia between 1973 and 1977 and have shown that in this region younger patients have a much better prognosis when compared with older groups with the same disease. When age-correction is applied, their prognosis still appears no worse than in older patients. This has implications in the management of patients with this common cancer. [ABSTRACT FROM AUTHOR]

The article focuses on standard deviation and standard error and their interpretation, usage and reporting.

Understanding how to interpret diagnostic test accuracy studies is a key skill that health practitioners need to develop in order to undertake evidence-based practice.[1] In this article we guide the reader through how to interpret a diagnostic test accuracy study, including the potential for bias. Typically, tests with higher sensitivity have lower specificity and vice versa, so the main purpose of applying the test must be considered when deciding on choice of test.[5] A highly specific test (Sp) returning a positive result (P) effectively rules in the diagnosis (SpPin); for example, meningococcal rash in meningococcal meningitis. Although we have focused on single diagnostic test accuracy studies so far, ideally we should seek out systematic reviews of the test we are interested in, so that we have estimates of test accuracy based on the totality of the evidence. When the spectrum of disease in the study population does not represent the intended population where the test will be used, test accuracy estimates may not be applicable. [Extracted from the article]

Douglass Hanly Moir is a large Australian laboratory which has recently introduced the ThinPrep Imaging System (TPI) for reading ThinPrep slides, which is still performed using a split-sample technique. The Imager is a computerized system which identifies 22 fields for the cytologist to review using automated light microscopy. We compared the accuracy of TPI and conventional cytology (CC) during normal laboratory operation. The ThinPrep sample was prepared after taking a conventional Pap smear. TPI and CC reading was done without knowledge of the result of the other reading. The final cytology report issued to the referring doctor reflected the more severe of these two results. Histology results for all cases in which TPI and CC cytology results showed more than minimal disagreement were sought from the NSW Pap Test Register. Of 55 164 split sample pairs, 3.1% of CC of slides and 1.8% of TPI slides were unsatisfactory. There were 1758 women for whom there was more than minimal discrepancy between TPI and CC cytology results. TPI gave the more severe result in 1193 of the 1758 cases. In cases where only one of each pair of discrepant cytology results was CIN1 or higher grade, TPI detected 133 cases of high-grade histology among 380 biopsies (35%), whereas CC detected 62 cases among 210 biopsies (29.5%). A repeat analysis based on reading of histology by one pathologist blinded to initial Pap smear result showed a similar result. Reading times were measured over 5 months for both TPI and CC for twenty cytologists who read both types of smears. On average, they read 13.3 TPI slides per hour and 6.1 CC slides per hour. This study provides evidence that cervical cytology read using the TPI detects more histological high-grade disease than does CC. Further evidence shows that reading times are significantly reduced for cytologists using the TPI. [ABSTRACT FROM AUTHOR]