The illusion of competency versus the desirability of expertise: Seeking a common standard for support professions in sport

In this paper we examine and challenge the competency-based models which currently dominate accreditation and development systems in sport support disciplines, largely the sciences and coaching. Through consideration of exemplar shortcomings, the limitations of competency-based systems are presented as failing to cater for the complexity of decision making and the need for proactive experimentation essential to effective practice. To provide a better fit with the challenges of the various disciplines in their work with performers, an alternative approach is presented which focuses on the promotion, evaluation and elaboration of expertise. Such an approach resonates with important characteristics of professions, whilst also providing for the essential ‘shades of grey’ inherent in work with human participants. Key differences between the approaches are considered through exemplars of evaluation processes. The expertise-focused method, although inherently more complex, is seen as offering a less ambiguous and more positive route, both through more accurate representation of essential professional competence and through facilitation of future growth in proficiency and evolution of expertise in practice. Examples from the literature are also presented, offering further support for the practicalities of this approach

Comparison of generalized estimating equations and quadratic inference functions using data from the National Longitudinal Survey of Children and Youth (NLSCY) database

<p>Abstract</p> <p>Background</p> <p>The generalized estimating equations (GEE) technique is often used in longitudinal data modeling, where investigators are interested in population-averaged effects of covariates on responses of interest. GEE involves specifying a model relating covariates to outcomes and a plausible correlation structure between responses at different time periods. While GEE parameter estimates are consistent irrespective of the true underlying correlation structure, the method has some limitations that include challenges with model selection due to lack of absolute goodness-of-fit tests to aid comparisons among several plausible models. The quadratic inference functions (QIF) method extends the capabilities of GEE, while also addressing some GEE limitations.</p> <p>Methods</p> <p>We conducted a comparative study between GEE and QIF via an illustrative example, using data from the "National Longitudinal Survey of Children and Youth (NLSCY)" database. The NLSCY dataset consists of long-term, population based survey data collected since 1994, and is designed to evaluate the determinants of developmental outcomes in Canadian children. We modeled the relationship between hyperactivity-inattention and gender, age, family functioning, maternal depression symptoms, household income adequacy, maternal immigration status and maternal educational level using GEE and QIF. Basis for comparison include: (1) ease of model selection; (2) sensitivity of results to different working correlation matrices; and (3) efficiency of parameter estimates.</p> <p>Results</p> <p>The sample included 795, 858 respondents (50.3% male; 12% immigrant; 6% from dysfunctional families). QIF analysis reveals that gender (male) (odds ratio [OR] = 1.73; 95% confidence interval [CI] = 1.10 to 2.71), family dysfunctional (OR = 2.84, 95% CI of 1.58 to 5.11), and maternal depression (OR = 2.49, 95% CI of 1.60 to 2.60) are significantly associated with higher odds of hyperactivity-inattention. The results remained robust under GEE modeling. Model selection was facilitated in QIF using a goodness-of-fit statistic. Overall, estimates from QIF were more efficient than those from GEE using AR (1) and Exchangeable working correlation matrices (Relative efficiency = 1.1117; 1.3082 respectively).</p> <p>Conclusion</p> <p>QIF is useful for model selection and provides more efficient parameter estimates than GEE. QIF can help investigators obtain more reliable results when used in conjunction with GEE.</p

Presence of an in situ component is associated with reduced biological aggressiveness of size-matched invasive breast cancer

Background:The metastatic propensity of invasive ductal carcinoma (IDC) of the breast correlates with axillary node involvement and with expression of the proliferation antigen Ki-67, whereas ductal carcinoma in situ (DCIS) is non-metastasising. To clarify whether concomitant DCIS affects IDC prognosis, we compared Ki-67 expression and node status of size-matched IDC subgroups either with DCIS (IDC-DCIS) or without DCIS (pure IDC).Methods:We analysed data from 1355 breast cancer patients. End points were defined by the association of IDC (with or without DCIS) with grade, nodal status, Ki-67, and ER/HER2.Results: Size-matched IDC-DCIS was more likely than pure IDC to be screen detected (P0.03), to occur in pre-menopausal women (P0.002), and to be either ER-positive (P0.002) or HER2-positive (P0.0005), but less likely to be treated with breast-conserving surgery (P0.004). Grade and Ki-67 were lower in IDC-DCIS than in pure IDC (P0.02), and declined as the DCIS enlarged (P0.01). Node involvement and lymphovascular invasion in IDC-DCIS increased with the size ratio of IDC to DCIS (P0.01). A 60-month cancer-specific survival favoured IDC-DCIS over size-matched pure IDC (97.4 vs 96.0%).Conclusion:IDC co-existing with DCIS is characterised by lower proliferation and metastatic potential than size-matched pure IDC, especially if the ratio of DCIS to IDC size is high. We submit that IDC-DCIS is biologically distinct from pure IDC, and propose an incremental molecular pathogenesis of IDC-DCIS evolution involving an intermediate DCIS precursor that remains dependent for replication on upstream mitogens. © 2010 Cancer Research UK All rights reserved.published_or_final_versio

A primary care, multi-disciplinary disease management program for opioid-treated patients with chronic non-cancer pain and a high burden of psychiatric comorbidity

BACKGROUND: Chronic non-cancer pain is a common problem that is often accompanied by psychiatric comorbidity and disability. The effectiveness of a multi-disciplinary pain management program was tested in a 3 month before and after trial. METHODS: Providers in an academic general medicine clinic referred patients with chronic non-cancer pain for participation in a program that combined the skills of internists, clinical pharmacists, and a psychiatrist. Patients were either receiving opioids or being considered for opioid therapy. The intervention consisted of structured clinical assessments, monthly follow-up, pain contracts, medication titration, and psychiatric consultation. Pain, mood, and function were assessed at baseline and 3 months using the Brief Pain Inventory (BPI), the Center for Epidemiological Studies-Depression Scale scale (CESD) and the Pain Disability Index (PDI). Patients were monitored for substance misuse. RESULTS: Eighty-five patients were enrolled. Mean age was 51 years, 60% were male, 78% were Caucasian, and 93% were receiving opioids. Baseline average pain was 6.5 on an 11 point scale. The average CESD score was 24.0, and the mean PDI score was 47.0. Sixty-three patients (73%) completed 3 month follow-up. Fifteen withdrew from the program after identification of substance misuse. Among those completing 3 month follow-up, the average pain score improved to 5.5 (p = 0.003). The mean PDI score improved to 39.3 (p < 0.001). Mean CESD score was reduced to 18.0 (p < 0.001), and the proportion of depressed patients fell from 79% to 54% (p = 0.003). Substance misuse was identified in 27 patients (32%). CONCLUSIONS: A primary care disease management program improved pain, depression, and disability scores over three months in a cohort of opioid-treated patients with chronic non-cancer pain. Substance misuse and depression were common, and many patients who had substance misuse identified left the program when they were no longer prescribed opioids. Effective care of patients with chronic pain should include rigorous assessment and treatment of these comorbid disorders and intensive efforts to insure follow up

Food-dependent, exercise-induced gastrointestinal distress

Among athletes strenuous exercise, dehydration and gastric emptying (GE) delay are the main causes of gastrointestinal (GI) complaints, whereas gut ischemia is the main cause of their nausea, vomiting, abdominal pain and (blood) diarrhea. Additionally any factor that limits sweat evaporation, such as a hot and humid environment and/or body dehydration, has profound effects on muscle glycogen depletion and risk for heat illness. A serious underperfusion of the gut often leads to mucosal damage and enhanced permeability so as to hide blood loss, microbiota invasion (or endotoxemia) and food-born allergen absorption (with anaphylaxis). The goal of exercise rehydration is to intake more fluid orally than what is being lost in sweat. Sports drinks provide the addition of sodium and carbohydrates to assist with intestinal absorption of water and muscle-glycogen replenishment, respectively. However GE is proportionally slowed by carbohydrate-rich (hyperosmolar) solutions. On the other hand, in order to prevent hyponatremia, avoiding overhydration is recommended. Caregiver's responsibility would be to inform athletes about potential dangers of drinking too much water and also advise them to refrain from using hypertonic fluid replacements

Common Variants at 10 Genomic Loci Influence Hemoglobin A(1C) Levels via Glycemic and Nonglycemic Pathways

OBJECTIVE Glycated hemoglobin (HbA1c), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA1c. We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA1c levels. RESEARCH DESIGN AND METHODS We studied associations with HbA1c in up to 46,368 nondiabetic adults of European descent from 23 genome-wide association studies (GWAS) and 8 cohorts with de novo genotyped single nucleotide polymorphisms (SNPs). We combined studies using inverse-variance meta-analysis and tested mediation by glycemia using conditional analyses. We estimated the global effect of HbA1c loci using a multilocus risk score, and used net reclassification to estimate genetic effects on diabetes screening. RESULTS Ten loci reached genome-wide significant association with HbA1c, including six new loci near FN3K (lead SNP/P value, rs1046896/P = 1.6 × 10−26), HFE (rs1800562/P = 2.6 × 10−20), TMPRSS6 (rs855791/P = 2.7 × 10−14), ANK1 (rs4737009/P = 6.1 × 10−12), SPTA1 (rs2779116/P = 2.8 × 10−9) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 × 10−9), and four known HbA1c loci: HK1 (rs16926246/P = 3.1 × 10−54), MTNR1B (rs1387153/P = 4.0 × 10−11), GCK (rs1799884/P = 1.5 × 10−20) and G6PC2/ABCB11 (rs552976/P = 8.2 × 10−18). We show that associations with HbA1c are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B). The seven nonglycemic loci accounted for a 0.19 (% HbA1c) difference between the extreme 10% tails of the risk score, and would reclassify ∼2% of a general white population screened for diabetes with HbA1c. CONCLUSIONS GWAS identified 10 genetic loci reproducibly associated with HbA1c. Six are novel and seven map to loci where rarer variants cause hereditary anemias and iron storage disorders. Common variants at these loci likely influence HbA1c levels via erythrocyte biology, and confer a small but detectable reclassification of diabetes diagnosis by HbA1c

A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease

Existing knowledge of genetic variants affecting risk of coronary artery disease (CAD) is largely based on genome-wide association studies (GWAS) analysis of common SNPs. Leveraging phased haplotypes from the 1000 Genomes Project, we report a GWAS meta-analysis of 185 thousand CAD cases and controls, interrogating 6.7 million common (MAF>0.05) as well as 2.7 million low frequency (0.005<MAF<0.05) variants. In addition to confirmation of most known CAD loci, we identified 10 novel loci, eight additive and two recessive, that contain candidate genes that newly implicate biological processes in vessel walls. We observed intra-locus allelic heterogeneity but little evidence of low frequency variants with larger effects and no evidence of synthetic association. Our analysis provides a comprehensive survey of the fine genetic architecture of CAD showing that genetic susceptibility to this common disease is largely determined by common SNPs of small effect siz