Introduction: The vast majority of patients presenting with dizziness to the emergency department (ED) are due to a benign self-limiting process. However, up to 5% have a serious central neurological cause. Our goal was to assess the sensitivity of clinical exam for a central cause in adult patients presenting to the emergency department with dizziness. Methods: At a tertiary care ED we performed a medical records review (Sep 2014- Mar 2018) including adult patients with dizziness (vertigo, unsteady, lightheaded), excluding those with symptoms >14days, recent trauma, GCS < 15, hypotensive, or syncope/loss of consciousness. 5 trained reviewers used a standardized data collection sheet to extract data. Individual patient data were linked with the Institute of Clinical Evaluation Science (ICES) database. Our outcome was a central cause defined as: ischemic stroke (IS), transient ischemic attack (TIA), brain tumour, intra cerebral haemorrhage (ICH), or multiple sclerosis (MS) diagnosed on either neurology assessment, computed tomography, magnetic resonance imaging, or diagnostic codes related to central causes found within ICES. A sample size of 1,906 was calculated based on an expected prevalence of 3% with an 80% power and 95% confidence interval to detect an odds ratio greater than 2. Univariate analysis and logistic regression were performed. Results: 3,109 were identified and 2,307 patients included (mean 57 years SD ± 20, Female 59.1%, Kappa 0.91). 62 central causes (IS 56.5%, TIA 14.5%, Tumour 11.3%, MS 9.7%, ICH 6.5%) of dizziness were identified. Imaging was performed in 945(42%) and neurology assessment in 42 (1.8%). ICES yielded no new diagnoses of a central cause for dizziness. Multivariate logistic regression found 11 high-risk findings associated with a central cause; history of IS/TIA (OR 3.8 95%CI 1.7-8.2), cancer (OR 3.2 95%CI 1.4-7.2), dyslipidemia (OR 2.3 95%CI 1.2-4.4), symptoms of visual changes (OR 2.1 95%CI 1.5-6.3), dysarthria (OR 9.1 95%CI 3-27.4), vomiting (OR 2 95%CI 1-3.7), motor deficit (OR 7.7 95%CI 2.9-20.2), sensory deficit (OR 28.9 95%CI 7.4-112.9), nystagmus (OR 3.3 95%CI 1.6-6.7), ataxia (OR 2.5 95%CI 1.3-4.9) and unable to walk 3 steps unaided (OR 3.4 95%CI 1.4-8.5). Absence of these findings had a sensitivity of 100% (95%CI 92.5-100%) for ICH, IS, Tumour and 95.2% (86.5-98.9) if including TIA and MS. Specificity was 51.5% (95%CI 49.4-53.6%). Conclusion: Clinical exam is highly sensitive for identifying patients without a central etiology for their dizziness.

Field validation studies were conducted in seven Mississippi environments at three application timings to confirm postemergence (POST) recommendations generated by the computer herbicide decision aids HERB and SWC. HERB and SWC agreed on herbicide treatments in only 14% of the location–application timing combinations. Weed scientists involved in the study agreed on treatment recommendations approximately 33% of the time. The HERB model agreed with a faculty member on only one herbicide treatment, while the SWC model was slightly more agreeable in this regard. Subsequent weed flushes, varied production practices, and delayed weed emergence accounted for a majority of the underestimated predictions given by HERB. Only 55% of the predicted values presented for estimated weed control ratings were similar to actual weed control ratings. Over 75% of the predictions that differed from actual weed control values were underpredictions. Recommendations from both computer models were effective in reducing yield loss below that of the untreated check, and recommendations from the HERB model generally improved yield more than those from the SWC model in most instances. HERB and SWC predictions of yield losses with no weed control were not significantly different from the actual yield loss from the untreated check in nine of the 12 instances at Starkville, seven of the 12 instances at Brooksville, six of the 12 instances at Newton, and three of the six instances at Hollandale. The HERB model estimated yield loss similar to that of the actual yield loss 83% of the time, while predictions from the SWC model were accurate 76% of the time. HERB overestimated yield loss in six of 21 application timing–experiment combinations and underestimated yield loss only once. Yield loss was overpredicted as high as 78%. SWC overpredicted yield loss in five of 21 instances and also underestimated in five instances. SWC did not overpredict yield loss to the same magnitude as HERB in many instances.

Crop yield loss–weed density relationships critically influence calculation of economic thresholds and the resulting management recommendations made by a bioeconomic model. To examine site-to-site and year-to-year variation in winter Triticum aestivum L. (winter wheat)–Aegilops cylindrica Host. (jointed goatgrass) interference relationships, the rectangular hyperbolic yield loss function was fit to data sets from multiyear field experiments conducted at Colorado, Idaho, Kansas, Montana, Nebraska, Utah, Washington, and Wyoming. The model was fit to three measures of A. cylindrica density: fall seedling, spring seedling, and reproductive tiller densities. Two parameters: i, the slope of the yield loss curve as A. cylindrica density approaches zero, and a, the maximum percentage yield loss as A. cylindrica density becomes very large, were estimated for each data set using nonlinear regression. Fit of the model to the data was better using spring seedling densities than fall seedling densities, but it was similar for spring seedling and reproductive tiller densities based on the residual mean square (RMS) values. Yield loss functions were less variable among years within a site than among sites for all measures of weed density. For the one site where year-to-year variation was observed (Archer, WY), parameter a varied significantly among years, but parameter i did not. Yield loss functions differed significantly among sites for 7 of 10 comparisons. Site-to-site statistical differences were generally due to variation in estimates of parameter i. Site-to-site and year-to-year variation in winter T. aestivum–A. cylindrica yield loss parameter estimates indicated that management recommendations made by a bioeconomic model cannot be based on a single yield loss function with the same parameter values for the winter T. aestivum-producing region. The predictive ability of a bioeconomic model is likely to be improved when yield loss functions incorporating time of emergence and crop density are built into the model's structure.

The concept of thresholds has many applications in weed science, depending on the response being measured. The most common adjectives used to describe thresholds are damage, economic, period, and action. Damage threshold is the term used to define the weed population at which a negative crop yield response is detected. An economic threshold is the weed population at which the cost of control is equal to the crop value increase from control of the weeds present. Economic threshold may be used to describe short-term effects of weed interference occurring in a single growing season, or multiple-season effects including some cost associated with seed produced by uncontrolled plants. The term period threshold implies that there are times during the crop cycle in which weeds are more or less damaging than at others. Action threshold is the point at which some control action is initiated, and usually includes economic considerations along with other less tangible factors such as aesthetics, risk aversion, or sociological pressures. Regardless of the type, thresholds imply that weed effects are population dependent, and as such, allow some type of prediction to be made relative to the consequences of control decisions. One successful approach to the implementation of thresholds has been through the development of computerized decision-aid software. These programs allow users to compare economic and environmental consequences of potential control actions before committing to one particular decision.

A weed emergence prediction model, WeedCast, was used as a decision aid to schedule potato cultivation with and without herbicides at Wooster, OH, USA; Charlottetown, PE, Canada; and Fargo, ND, USA, from 2001 to 2003. Studies were laid out in a split-plot design with herbicides (±) forming the main plots and cultivation timing as subplots. Cultivation was done at 15, 30, or 60% of predicted weed emergence. Subplots were either left unsprayed or treated with metolachlor + metribuzin at 1.68 + 0.5 kg ai ha−1 and only cultivated at predetermined timing. Cultivation timing was based on predicted emergence of common lambsquarters at Wooster and Charlottetown, whereas eastern black nightshade was the indicator weed at Fargo. Weed control for the different cultivation timings varied among sites and years and was consistently better in plots where herbicides were followed by cultivation. Cultivation alone resulted in poor weed control and significantly reduced potato tuber yield compared with those in plots where weed control also included herbicides. Use of herbicides followed by cultivation and hilling increased tuber yield by 4.6, 4.3, and 8.7 t ha−1, when cultivations were done at 15, 30, and 60% of predicted weed emergence, respectively, and 12.2 t ha−1 for hilled-only plots. The average potato yield increase at Charlottetown was 9.7, 5.9, 6.9, and 7.4 t ha−1 for hilled-only plots and for hilled after cultivations at 15, 30, and 60% of predicted weed emergence with herbicides, respectively. There was no apparent pattern for treatment effects at Fargo, and the potato tuber yields were greatly reduced mainly because of excessive precipitation during potato establishment. Use of WeedCast as a decision-aid tool could be an asset in determining when to do the first and subsequent cultivations. It may work best for growers who use cultivations in potato to remove weeds that were not controlled by herbicides.

Three models that empirically predict crop yield from crop and weed density were evaluated for their fit to 30 data sets from multistate, multiyear winter wheat–jointed goatgrass interference experiments. The purpose of the evaluation was to identify which model would generally perform best for the prediction of yield (damage function) in a bioeconomic model and which model would best fulfill criteria for hypothesis testing with limited amounts of data. Seven criteria were used to assess the fit of the models to the data. Overall, Model 2 provided the best statistical description of the data. Model 2 regressions were most often statistically significant, as indicated by approximate F tests, explained the largest proportion of total variation about the mean, gave the smallest residual sum of squares, and returned residuals with random distribution more often than Models 1 and 3. Model 2 performed less well based on the remaining criteria. Model 3 outperformed Models 1 and 2 in the number of parameters estimated that were statistically significant. Model 1 outperformed Models 2 and 3 in the proportion of regressions that converged on a solution and more readily exhibited an asymptotic relationship between winter wheat yield and both winter wheat and jointed goatgrass density under the constraint of limited data. In contrast, Model 2 exhibited a relatively linear relationship between yield and crop density and little effect of increasing jointed goatgrass density on yield, thus overpredicting yield at high weed densities when data were scarce. Model 2 had statistical properties that made it superior for hypothesis testing; however, Model 1's properties were determined superior for the damage function in the winter wheat–jointed goatgrass bioeconomic model because it was less likely to cause bias in yield predictions based on data sets of minimum size.

Nascent research on the profitability of site-specific weed management has focused on reduced herbicide use, ignoring significant information costs for scouting, making treatment maps, and patch herbicide application. Including these information costs results in few, if any, studies, fully covering added costs with herbicide savings. Real-time, sensor-driven site-specific herbicide management promises to overcome many of the scouting and map-making costs so long as weed species recognition accuracy continues to improve and commercial capital costs to decline. Two means to increase revenues are possible from site-specific weed management: value-added crop products (e.g., herbicide-free, nontransgenic crops) and environmental stewardship payments from government programs. Site-specific ecological weed management offers a more fruitful means to garner both higher prices from “green” consumers and revenues for sound environmental stewardship. It entails using spatial tools to enhance habitat for weed predators and diseases and their vectors, as well as to grow crops that are better able to compete with weeds. The most economically promising areas for future weed management will draw upon both spatial and developmental management tools.

To manage jointed goatgrass (Aegilops cylindrica), growers need an action threshold decision aid to assist them in deciding when to rotate to a spring crop instead of growing winter wheat (Triticum aestivum). A study was conducted in Washington from 1992 to 1995 to predict winter wheat yield loss based on a jointed goatgrass population in the previous year's winter wheat crop. To describe the relationship between jointed goatgrass density in year 1 and the resulting winter wheat yield loss in year 2, data were fitted to a rectangular hyperbolic model. First year jointed goatgrass densities in winter wheat of 1 to 5 plants/m2 led to a second year winter wheat yield loss of 3 to 30%. Yield loss was greater in a dry year than in a normal to wet year. To initiate an action threshold for jointed goatgrass successfully, growers need to incorporate data from this study with their knowledge of yield potential, price dockage of the subsequent winter wheat crop, and profitability of alternative spring crop rotations.

The Wheat and Stocker Cattle Analyzer is a microcomputer decision aid for evaluating interrelated wheat production and stocker cattle grazing decisions under yield, weight gain, and price uncertainty. An important feature of the model is that wheat commodity program provisions are incorporated into the analysis. A wide range of alternatives including wheat production for grain only, owned stocker cattle grazing, and wheat pasture leasing can be evaluated by the program.