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When Computers Were Human
Before Palm Pilots and iPods, PCs and laptops, the term \"computer\" referred to the people who did scientific calculations by hand. These workers were neither calculating geniuses nor idiot savants but knowledgeable people who, in other circumstances, might have become scientists in their own right. When Computers Were Human represents the first in-depth account of this little-known, 200-year epoch in the history of science and technology. Beginning with the story of his own grandmother, who was trained as a human computer, David Alan Grier provides a poignant introduction to the wider world of women and men who did the hard computational labor of science. His grandmother's casual remark, \"I wish I'd used my calculus,\" hinted at a career deferred and an education forgotten, a secret life unappreciated; like many highly educated women of her generation, she studied to become a human computer because nothing else would offer her a place in the scientific world. The book begins with the return of Halley's comet in 1758 and the effort of three French astronomers to compute its orbit. It ends four cycles later, with a UNIVAC electronic computer projecting the 1986 orbit. In between, Grier tells us about the surveyors of the French Revolution, describes the calculating machines of Charles Babbage, and guides the reader through the Great Depression to marvel at the giant computing room of the Works Progress Administration. When Computers Were Human is the sad but lyrical story of workers who gladly did the hard labor of research calculation in the hope that they might be part of the scientific community. In the end, they were rewarded by a new electronic machine that took the place and the name of those who were, once, the computers.
eBook
Empire of the sum : the rise and reign of the pocket calculator
\"The hidden history of the pocket calculator -- a device that ushered in modern mathematics, helped build the atomic bomb, and went with us to the moon -- and the mathematicians, designers, and inventors who brought it to life.\" -- back cover
Book
The computer book : from the abacus to artificial intelligence, 250 milestones in the history of computer science
\"Two expert authors, with decades of experience working in computer research and innovation, explore topics including the Sumerian abacus, the first spam message, Morse code, cryptography, early computers, Isaac Asimov's laws of robotics, UNIX and early programming languages, movies, video games, mainframes, minis and micros, hacking, virtual reality, and more\"-- Provided by publisher.
Book
Family health history: underused for actionable risk assessment
Family health history (FHH) is the most useful means of assessing risk for common chronic diseases. The odds ratio for risk of developing disease with a positive FHH is frequently greater than 2, and actions can be taken to mitigate risk by adhering to screening guidelines, genetic counselling, genetic risk testing, and other screening methods. Challenges to the routine acquisition of FHH include constraints on provider time to collect data and the difficulty in accessing risk calculators. Disease-specific and broader risk assessment software platforms have been developed, many with clinical decision support and informatics interoperability, but few access patient information directly. Software that allows integration of FHH with the electronic medical record and clinical decision support capabilities has provided solutions to many of these challenges. Patient facing, electronic medical record, and web-enabled FHH platforms have been developed, and can provide greater identification of risk compared with conventional FHH ascertainment in primary care. FHH, along with cascade screening, can be an important component of population health management approaches to overall reduction of risk.
Journal Article
Performance of PCA3 and TMPRSS2:ERG Within the Prostate Cancer Prevention Trial Risk Calculator Version 2 in a Lithuanian Cohort
Prostate cancer (PCa) remains a significant health concern due to its high incidence and associated mortality. Conventional screening approaches, like PSA testing, often lack specificity, resulting in unnecessary biopsies and overtreatment. This study seeks to overcome these limitations by assessing the integration of novel urinary biomarkers into established risk prediction models.
This study aimed to evaluate the performance of incorporating urinary biomarkers - prostate cancer antigen 3 (PCA3) and transmembrane serine protease 2 (TMPRSS2) gene and ETS-related gene (ERG) fusion genes (T:E) - into the Prostate Cancer Prevention Trial Risk Calculator version 2 (PCPTRC2) in a Lithuanian cohort to enhance the detection of clinically significant prostate cancer (csPCa).
A single-centre prospective study included 246 men scheduled for initial prostate biopsy between January 2021 and August 2024 due to elevated total PSA levels or abnormal digital rectal examination (DRE). Following ethical approval and informed consent, urinary samples were collected post-DRE and analysed for PCA3 and T:E. Each patient's risk was calculated using the basic PCPTRC2 and updated versions incorporating biomarkers. Biopsies were performed based on multiparametric magnetic resonance imaging (mpMRI) findings.
Of 209 biopsy samples analysed, 111 (53.1%) were diagnosed with csPCa. The AUC for PCa detection was 59.6% for the original PCPTRC2, improving to 76.2% with PCA3 and further to 79.5% when both PCA3 and T:E were included. Both updated versions demonstrated significantly higher sensitivity compared to the original (p<0.001). However, no significant differences were noted in distinguishing csPCa from non-csPCa.
Incorporating PCA3 and T:E into PCPTRC2 substantially enhances diagnostic accuracy for detecting PCa in biopsy-naïve patients. Despite limitations, these findings underscore the potential for optimizing risk calculators in clinical practice, advocating for larger cohorts to validate these results.
Journal Article
Performance of three breast cancer risk assessment tools in US Black women
Background
Breast cancer risk prediction models aid identification of high-risk women for earlier or more frequent screening. The two most commonly used U.S. models appear to perform less well in Black women, possibly because Black women have a lower proportion of estrogen-receptor positive breast cancer. We recently developed and externally validated a model for use in Black women (BWHS model). Here, we compare performance metrics of that model with the other two models using data from a large cohort of Black women.
Results
We assessed the NCI Breast Cancer Risk Assessment Tool (BCRAT) using the option for Black women, the IBIS model, including clinical variables only, and the BWHS model in data from a cohort of 50,235 Black women followed over four sequential 5 year periods. Predictors were updated at the start of each 5 year period, and 2041 invasive breast cancers occurred. Calibration metrics, expected over observed number of cancers, were 0.99 (0.94–1.04), 0.97 (0.93–1.02), and 1.13 (1.08–1.18) from the BWHS, BCRAT, and IBIS models, respectively. The metrics for discriminatory accuracy, age-adjusted area under the curve (AUC), were 0.58 (0.56–0.59), 0.56 (0.55–0.57), and 0.56 (0.55–0.57), from the BWHS, BCRAT, and IBIS models, respectively.
Conclusions
In this comparison, the BWHS model had better calibration and discrimination than BCRAT and IBIS, including among women age < 40, indicating a benefit to using the BWHS model for Black women. While models that incorporate mammographic features may have higher AUCs, models based on clinical factors are beneficial for young women and those without available mammography data.
Journal Article