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Browsing by Subject "bias"

Now showing 1 - 6 of 6
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    Analysis And Control Of Temporal Biases In Surgical Skill Evaluation
    (2020-05) Kelly, Jason
    Objectively and accurately assessing the technical skill of a surgeon is critically important. The current gold standard relies on a panel of expert surgeons evaluating surgical video footage using structured survey instruments. This is a prohibitively time-consuming process, thus leaving the majority of procedures unevaluated. Previous methods of evaluating skill remain prone to bias towards a surgeons' speed or task times, fueling the need to investigate the mechanisms underlying human motion in favor of techniques impervious to biases. The research objective of this work is to investigate the effects of time and speed on the relative accuracy of both human and computational methods of measuring surgical technical skill. Human methods consist of both expert and non-expert raters (faculty surgeons and Amazon Mechanical Turk crowd workers respectively). Computational methods consist of both neurophysiologically-derived measures from other disciplines and recent model-free machine learning methods. This research objective is pursued by the following four specific aims: Specific Aim 1: Determine whether surgical motion segments are directly correlated to tangential velocity prediction models, and if the result is impervious to surgeon speed. The objective was to test the null hypothesis that there is no relationship between the minimum jerk trajectory velocity prediction model and increases in technical skills proficiency. Prior work in human reaching suggests that adherence to the minimum jerk model should increase as technical skill increases, for proficiency of reaching motions in stroke rehabilitation. This thesis investigates whether this phenomenon holds true in surgical technical skill during simulated dry lab tasks. Specific Aim 2: Implement a classification algorithm which uses recorded data to classify surgical skill in a manner which is impervious to task time. It was hypothesized that recent machine learning algorithms which exploit temporal duration, used on kinematic data from dry lab laparoscopic training tasks, would increase the performance of the current state-of-the-art computational methods of classifying surgical skill. HASH(0x3de9178) Specific Aim 3: Determine how human ratings of surgical tasks are affected by video playback speed and duration. It was hypothesized that the perceived skill of a surgeon followed a unimodal function, in which human raters would experience an increase in perceived surgical skill as the speed of a surgical task video approaches the function's maximum, then immediately decreasing once being aware of the video's playback manipulation. Specific Aim 4: Measure the effect that pre-operative warm-up using validated virtual-reality simulator tasks has on practicing surgeons in real robotic surgeries as measured by the most accurate, least-biased methods detailed in the previous specific aims and prior art. This tested the hypothesis that pre-operative warm-up results in a measurable improvement in surgical technical skill among practicing surgeons (no novices) using surgical robots from live patients. This research concluded firstly that neurophsyiologically-derived models of skill, specifically the minimum jerk model, do not necessarily extend to surgical settings. Surprisingly this research found the opposite, that surgeon experts exhibit movements which deviate further from the minimum jerk model. Second, a classification algorithm was created, using a bidirectional long short-term memory network which controls for task time, and is capable of classifying experts and novices with over 95% accuracy for tasks most resembling real surgery. This research brought about questions of label noise and accuracy, and emphasizes the importance of properly labeled data for machine learning algorithms. It was found that humans appear to have a speed bias in rating surgeons both for laparoscopic surgical training tasks as well as real robotic surgery procedures. Unexpectedly, this effect was more substantial for more expert performances and negligible for novice performers. Counter to our original hypothesis, expectations derived from biological motion models -- that skill discrimination capability would unimodally decrease when video playback was obviously artificially sped up -- were not met. Observer ability to discriminate skill continues well after people are cognizant of a video being played at quicker speeds and no discernible difference between biological-motion-relevant question groups (e.g. motion fluidity) and other questions appeared. Finally, a new dataset of robotic surgeries was introduced, with 343 videos of robotic surgeries including tooltip kinematic data. Evidence obtained from motion metrics, crowd ratings, and faculty surgeon ratings suggest that no measurable warm-up effect was present in our population of 41 practicing surgeons; no evidence supported the use of warm-up.
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    Effect of sampling protocol and volunteer bias when sampling for macroinvertebrates
    (2008-07-08) Nerbonne, Julia, F.; Ward, Brad; Ollila, Ann; Williams, Mary; Vondracek, Bruce
    We evaluated the efficacy of different field sampling approaches for volunteers sampling macroinvertebrates in low-gradient streams.We used a series of analytical metrics to compare results using the Environmental Protection Agency (EPA) multihabitat, the Minnesota Pollution Control Agency multihabitat, and EPA single-habitat sampling protocols. We also investigated the effect of 2 scenarios in which volunteers fail to follow (and potentially bias) the widely used EPA multihabitat protocol by including either more snag and vegetated banks or more run and riffle habitat than prescribed by the protocol. We collected jab samples from cobble, snags, vegetated banks, submerged macrophytes, and sand in 4 contiguous 125-m reaches in an Anoka sand-plain stream in Minnesota. We identified up to 100 macroinvertebrates in each jab sample to family. We subjected a parent population of 40 jab samples/reach to a bootstrap analysis to sample and create metric or index scores 100 times without replacement for each of the 3 volunteer sampling methods and 2 biased scenarios. The EPA multihabitat protocol and the biased scenario in which woody debris and bank vegetation were oversampled yielded the highest diversity of organisms, whereas the biased scenario in which riffle and run habitats were oversampled yielded the lowest diversity. The EPA multihabitat protocol used correctly was more likely to indicate ‘‘good’’ water quality (on the basis of the EPA muddy-bottom narrative assessment tool designed for volunteers) than either biased sampling scenario. This result illustrates that poor field methods could result in underestimation of water quality.
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    Estimating themissing species bias in plant trait measurements
    (Wiley, 2015) Sandel, Brody; Gutiérrez, Alvaro G; Reich, Peter B; Schrodt, Franziska; Dickie, John; Kattge, Jens
    Aim Do plant trait databases represent a biased sample of species, and if so, can that bias be corrected? Ecologists are increasingly collecting and analysing data on plant functional traits, and contributing them to large plant trait databases. Many applications of such databases involve merging trait measurements with other data such as species distributions in vegetation plots; a process that invariably produces matrices with incomplete trait and species data. Typically, missing data are simply ignored and it is assumed that the missing species are missing at random. Methods Here, we argue that this assumption is unlikely to be valid and propose an approach for estimating the strength of the bias regarding which species are represented in trait databases. The method leverages the fact that, within a given database, some species have many measurements of a trait and others have few (high vs low measurement intensity). In the absence of bias, there should be no relationship between measurement intensity and trait values. We demonstrate the method using five traits that are part of the TRY database, a global archive of plant traits. Our method also leads naturally to a correction for this bias, which we validate and apply to two examples. Results Specific leaf area and seed mass were strongly positively biased (frequently measured species had higher trait values than rarely measured species), leaf nitrogen per unit mass and maximum height were moderately negatively biased, and maximum photosynthetic capacity per unit leaf area was weakly negatively biased. The bias-correction method yielded greatly improved estimates in the validation tests for the two most biased traits. Further, in our two applications, ecological interpretations were shown to be sensitive to uncorrected bias in the data. Conclusions Species inclusion in trait databases appears to be strongly biased in some cases, and failure to correct this can lead to incorrect conclusions.
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    Single Fathers and Employment Discrimination: Penalized or Protected?
    (2023-05) Iztayeva, Aimzhan
    This research examines employment discrimination against custodial single fathers in the United States. Fatherhood is associated with breadwinning, and employers expect full work commitment. Yet, caregiving constrains breadwinning because family demands are time-consuming and labor-intensive. This raises the following questions: In what ways, if at all, do employers discriminate against single fathers with primary caregiving responsibility? How do custodial single fathers experience their roles as primary breadwinners and primary caregivers? My dissertation offers answers to these questions by considering how gender, breadwinning, and caregiving roles operate in employers’ hiring preferences and single fathers’ efforts to meet work and caregiving demands.
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    Substance Use Transmission and Outcomes: Using Genetically Informative Research Designs for Causal Inference with Observational Data
    (2019-07) Saunders, Gretchen
    One of the most difficult, yet arguably the most important aspect of research is the issue of causal inference using observational data. For phenotypes like substance use, in which it is impractical or unethical to conduct randomized controlled trials, understanding the causal mechanisms that influence substance use behavior as well as the outcomes caused by these behaviors remains difficult. The current work explores how genetically related samples can be exploited to better understand the causal effects of environmental factors on adult outcomes related to early substance use. In Study 1, polygenic risk scores for alcohol and tobacco use are used to identify a genetic nurture effect of parental smoking initiation on offspring alcohol and tobacco use in a large parent-offspring sample. The effect of parental genotype on offspring use is mediated by parental socioeconomic status (SES), suggesting that rearing SES, or the resources higher SES provide, may causally influence substance use in adolescence. Study 2 is a methodological exploration of co-twin control (CTC) designs, in which an exposure- outcome effect is decomposed into a within-twin pair and between-twin pair effect. A limitation of the CTC design is that it cannot implicitly control for environmental factors that are not perfectly shared within a twin pair, the presence of which may bias CTC findings. We use analytical derivations and simulations to show that while inclusion of a covariate as a proxy measure of a non-shared environmental confounder will always reduce bias, results from CTC studies will continue to be biased away from the null to at least some extent in most practical situations. Interpretation and suggestions for use of CTC, and more generally between-within, models are discussed. Finally, in Study 3 we use a large sample of twins to investigate the adult socioeconomic outcomes related to adolescent substance use. Using the co-twin control (CTC) design we find that within monozygotic (MZ) twin pairs, who share all genetic and common environmental factors, the twin who consumes more tobacco and alcohol in adolescence has lower educational attainment and occupational status in adulthood compared to their lesser using co-twin, consistent with a causal effect of early substance use on later socioeconomic outcomes. We focus on interpretation of these results in the context of findings from Study 2.
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    Understanding Geographic Bias in Crowd Systems
    (2017-12) Thebault-Spieker, Jacob
    Crowd platforms are increasingly geographic, from the sharing economy to peer production systems like OpenStreetMap. Unfortunately, this means that existing geographic advantages or disadvantages (e.g. by income, urbanness, or race) may also impact these crowd systems. This thesis focuses on two primary themes: (1) how these geographic advantages and disadvantages interact with crowd platform services, and (2) how people’s geographic behavior within these platforms may lead to these biases being reflected. The first chapter in my thesis finds that sharing economy services fare less well in low-income, non-white, and more suburban areas. This chapter introduces the spatial Durbin model to the field of HCI, and shows that geographic factors like distance, socioeconomic status and demographics inform where sharing economy workers provide service. The second chapter in my thesis provides focuses on people in peer production communities contribute geographic content. By considering peer production as a spatial interaction process, this study finds that some kinds of content tend to be produced much more locally than others. Finally, my third contribution focuses on individual contributor behavior, and shows geographic “born, not made” trends. People tend to be consistent in the places, and kinds of places (urban, and non-high poverty counties) they contribute. The findings of this third study help identify mechanisms for how geographic biases may come about. Looking forward, my work helps inform an exciting agenda of future work, including building systems that provide individual crowd members sufficient geographic context to counteract these worrying geographic biases.