Citations

Because physician turnover, especially during the first 5 years after joining a practice group, has been reported to be as high as 54%, we sought to identify and quantify the factors that lead hand surgeons to leave their practices. An eight-question practice survey was administered to 10 hand surgeons, all of whom had left their practices under various circumstances. All respondents were men (mean age, 43.2 years). Nine surgeons were in their first practice and one was in his second. Time in the practice ranged from 4 months to 6.5 years (mean time, 3.4 years). Questions about surgical practice issues were asked of all survey participants, who were then asked to quantify the influence of the issue on their decision to leave their own surgical practice with a Likert scale ranging from 1 (no influence) to 5 (greatly influenced the decision to leave). The strongest influences on the decision to leave a practice were interpersonal conflict (mean, 4.7 points) and lack of decision-making ability within the practice (mean, 4.2 points). The influence of partnership determination (mean, 3.3 points) was mixed, whereas staff support (mean, 2.9 points), salary agreements (mean, 2.4 points), employment contracts (mean, 1.6 points), and retirement benefits (mean, 1.0 points) played a lesser role in the surgeons' decisions to leave their practices.

SummaryOne of the most difficult laboratory challenges in the field of therapeutic cancer vaccines has been the development of uncomplicated/reproducible methods for the quantification of vaccine immunization efficacy in peripheral blood of cancer patients. Existing methods are limited by lack of functional information (tetramers), difficulties with standardization/reproducibility [enzyme-linked immunosorbent spot (ELISPOT)] and reliance on endogenous (sample-specific) antigen presentation (cytokine flow cytometry). Herein we present a reproducible method utilizing an artificial antigen-presenting cell platform for flow cytometry-based quantification of the frequency and activation status of peptide-specific cytotoxic T lymphocytes. The methodology [currently presented for cytomegalovirus human leucocyte antigen (HLA)-A2 cognant peptide antigens] allows simultaneous ex vivo quantification of activated (cytokine-producing) and inactive tetramer-positive T cells following HLA class I/peptide/CD28 stimulation independent of endogenous antigen presentation. The simplicity and reliability of the assay provide for high-throughput applications and automation. The utility and application of this method are discussed.

Purpose: Several Medline search strategies exist to retrieve complementary and alternative medicine (CAM) literature related to oncology. The objective of this study was to compare different search methods to ascertain the most optimal strategy. Methods: All clinical trials with CAM interventions in patients with cancer, published from 1965 to 2004, were abstracted using 4 different approaches. In the CAM filter search, the PubMed complementary medicine filter was used. The Ovid search was performed using a complex search strategy with the Ovid search engine. CAM keyword and not phytogenic searches involved the CAM filter search with the addition of the search terms “AND (complementary OR alternative)” and “NOT (antineoplastic agents, phytogenic), respectively. Articles were evaluated by 3 reviewers to ascertain whether they were clinical trials, the study intervention was related to CAM, and the condition prevented/treated was cancer related (inclusion criteria). Results: The CAM filter search retrieved 10 718 citations, Ovid retrieved 1190, CAM keyword retrieved 2895, and not phytogenic retrieved 1806. Compared to the CAM filter search, all other methods had significantly lower sensitivity (Ovid 48.3% ± 3.2%, CAM keyword 5.8% ± 1.5%, and not phytogenic 77.9% ± 2.7%, P < .001). The specificity of Ovid (38.4% ± 2.8%) and not phytogenic (40.8% ± 2.3%) searches was significantly higher ( P < .001) compared to CAM filter (8.8% ± 0.5%) and CAM keyword searches (1.9% ± 0.5%). Conclusion: The search strategy using PubMed’s complementary medicine filter, although comprehensive, lacks specificity; other methods, although more specific, lack sensitivity. Future indexing of all CAM clinical trials with a common medical subject heading term complementary medicine would enhance efficient retrieval of relevant citations.

Mild cognitive impairment is a topic of great activity from both clinical and research perspectives. It represents a transitional state between the cognitive changes of aging and the earliest clinical manifestations of dementia. We present a case for its inclusion in the Diagnostic and Statistical Manual of Mental Disorders (5th ed; DSM-V) based on clinical, outcome, epidemiological, neuroimaging, and pathophysiological data. The strongest case for inclusion can be made for the amnestic subtype, which is likely a clinical precursor of Alzheimer’s disease. Arguments are presented as to why mild cognitive impairment can be considered as an entity distinct from normal aging and from clinically probable Alzheimer’s disease and why it deserves consideration as a separate construct. In many respects, mild cognitive impairment fulfills criteria for inclusion more adequately than many other conditions currently codified in DSM-IV. Future research directions to help clarify some of the remaining uncertainties are proposed.

Abstract Motivation: The need for normalization in microarray experiments has been well documented in the literature. Currently, many analysis methods treat normalization and analysis as a series of steps, with summarized data carried forward to the next step. Results: We present a unified algorithm which incorporates normalization and class comparison in one analysis using probe level perfect match and mismatch data. The algorithm is based on calibration models common to most biological assays, and the resulting chip-specific parameters have a natural interpretation. We show that the algorithm fits into the statistical generalized linear models framework, describe a practical fitting strategy and present results of the algorithm applied to an example dataset as well as based on metrics used in affycomp. The algorithm ranks amongst the top third of the affycomp competitors, performing best in measures of bias. Availability: R functions are available on request from the authors. Contact: oberg.ann@mayo.edu

OBJECTIVE—We sought to systematically ascertain the quality of randomized controlled trials (RCTs) in diabetes. RESEARCH DESIGN AND METHODS—We identified the 10 most recently published trials as of 31 October 2003 in each of six general medical, five diabetes, and five metabolism and nutrition journals and further enriched our sample with 10 additional RCTs from each of five journals that published the most eligible RCTs in a year. We explored the association between trial characteristics and reporting quality using univariate analyses and a preplanned multivariate regression model. RESULTS—After excluding redundant reports of included trials and one trial that measured outcomes on the health system and not on patients, we included 199 RCTs: 119 assessed physiological and other laboratory outcomes, 42 assessed patient-important outcomes (e.g., morbidity and mortality, quality of life), and 38 assessed surrogate outcomes (e.g., disease progression or regression, HbA1c, cholesterol). Fifty-three percent were of low methodological quality, as were one-third (36–40%) of trials reporting patient-important or surrogate outcomes and two-thirds (64%) of laboratory investigations. Independent predictors of low quality were nonprofit funding source (odds ratio 3.1 [95% CI 1.5–6.2]), measure of physiological and laboratory outcomes (2.3 [1.2–4.4]), and cross-over design (2.3 [1.1–4.8]), all characteristics of laboratory clinical investigations. CONCLUSIONS—There is ample room for improving the quality of diabetes trials. To enhance the practice of evidence-based diabetes care, trialists need to pay closer attention to the rigorous implementation and reporting of important methodological safeguards against bias in randomized trials.