scholarly journals Unusual suspects: Real-time physiological evaluation of stressors during laparoscopic donor nephrectomy

2020 ◽  
Vol 15 (4) ◽  
Author(s):  
Claire Wilson ◽  
Saad Chahine ◽  
Sayra Cristancho ◽  
Shahid Aquil ◽  
Moaath Mandurah ◽  
...  
Keyword(s):  
Stress Response ◽  
Real Time ◽  
Electrodermal Activity ◽  
Donor Nephrectomy ◽  
Laparoscopic Donor Nephrectomy ◽  
High Stakes ◽  
Factorial Anova ◽  
Physiological Evaluation ◽  
Faculty Stress ◽  
Main Effects

Introduction: The purpose of this study was to document the variability of faculty surgeon electrodermal activity (EDA) peaks during laparoscopic donor nephrectomy (LDN) to determine the effect of case difficulty and learner expertise on the stress response. Methods: EDA for a single faculty surgeon was captured over 15 LDN cases using an Empatica E4 wristband. During each case, one of three transplant fellows (novice, intermediate, or expert level LDN expertise) participated. Difficulty was rated preoperatively as “low/moderate/high” by the faculty. EDA peaks were collected and analyzed; the frequency and magnitude of EDA peaks, case difficulty, and fellow expertise were compared using a two-way factorial ANOVA. Results: The main effects of learner expertise (F[2, 308]=11.27, p<0.001) and difficulty rating (F[2, 414]=15.13, p<0.001) were significant. The interaction between difficulty and expertise on faculty EDA peaks was also significant (F[3, 391]=14.29, p<0.001). The novice fellow resulted in higher faculty EDA levels compared to intermediate and expert fellows on low-difficulty cases, but not moderate- or high-difficulty cases. Conclusions: This is the first report examining faculty surgeon EDA across cases of varying difficulty and varying learner expertise during a high-stakes operation. EDA levels were inversely proportional to the expertise of the learner and case difficulty, suggestive of a significant impact of learner autonomy on faculty stress response.

1855: Laparoscopic Donor Nephrectomy: Impact of Warm Ischemia on Allograft Function

2004 ◽  
Vol 171 (4S) ◽  
pp. 490-490
Author(s):  
Mahesh C. Gael ◽  
J. Feng ◽  
David A. Goldfarb ◽  
lnderbir S. Gill
Keyword(s):  
Warm Ischemia ◽  
Donor Nephrectomy ◽  
Laparoscopic Donor Nephrectomy ◽  
Allograft Function

1952: Is Intraoperative Heparin Necessary During Laparoscopic Donor Nephrectomy?

2004 ◽  
Vol 171 (4S) ◽  
pp. 515-516 ◽  
Author(s):  
Andrew Steinberg ◽  
Anup P. Ramani ◽  
Sidney C. Abreu ◽  
Mete Kilciler ◽  
Mihir M. Desai ◽  
...  
Keyword(s):  
Donor Nephrectomy ◽  
Laparoscopic Donor Nephrectomy

Near-Perfect Automation: Investigating Performance, Trust, and Visual Attention Allocation

2021 ◽  
pp. 001872082110328
Author(s):  
Cyrus K. Foroughi ◽  
Shannon Devlin ◽  
Richard Pak ◽  
Noelle L. Brown ◽  
Ciara Sibley ◽  
...  
Keyword(s):  
Visual Attention ◽  
False Alarm ◽  
Real Time ◽  
Subjective Experience ◽  
Automated System ◽  
Automated Systems ◽  
High Stakes ◽  
Individual Level ◽  
Scan Pattern ◽  
Subjective Trust

Objective Assess performance, trust, and visual attention during the monitoring of a near-perfect automated system. Background Research rarely attempts to assess performance, trust, and visual attention in near-perfect automated systems even though they will be relied on in high-stakes environments. Methods Seventy-three participants completed a 40-min supervisory control task where they monitored three search feeds. All search feeds were 100% reliable with the exception of two automation failures: one miss and one false alarm. Eye-tracking and subjective trust data were collected. Results Thirty-four percent of participants correctly identified the automation miss, and 67% correctly identified the automation false alarm. Subjective trust increased when participants did not detect the automation failures and decreased when they did. Participants who detected the false alarm had a more complex scan pattern in the 2 min centered around the automation failure compared with those who did not. Additionally, those who detected the failures had longer dwell times in and transitioned to the center sensor feed significantly more often. Conclusion Not only does this work highlight the limitations of the human when monitoring near-perfect automated systems, it begins to quantify the subjective experience and attentional cost of the human. It further emphasizes the need to (1) reevaluate the role of the operator in future high-stakes environments and (2) understand the human on an individual level and actively design for the given individual when working with near-perfect automated systems. Application Multiple operator-level measures should be collected in real-time in order to monitor an operator’s state and leverage real-time, individualized assistance.

scholarly journals Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3956
Author(s):  
Youngsun Kong ◽  
Hugo F. Posada-Quintero ◽  
Ki H. Chon
Keyword(s):  
Real Time ◽  
Electrodermal Activity ◽  
Smartphone Application ◽  
Real Time Processing ◽  
Pain Stimulation ◽  
Significant Difference ◽  
Before And After ◽  
Electrical Pulses ◽  
Pain Detection ◽  
High Level

The subjectiveness of pain can lead to inaccurate prescribing of pain medication, which can exacerbate drug addiction and overdose. Given that pain is often experienced in patients’ homes, there is an urgent need for ambulatory devices that can quantify pain in real-time. We implemented three time- and frequency-domain electrodermal activity (EDA) indices in our smartphone application that collects EDA signals using a wrist-worn device. We then evaluated our computational algorithms using thermal grill data from ten subjects. The thermal grill delivered a level of pain that was calibrated for each subject to be 8 out of 10 on a visual analog scale (VAS). Furthermore, we simulated the real-time processing of the smartphone application using a dataset pre-collected from another group of fifteen subjects who underwent pain stimulation using electrical pulses, which elicited a VAS pain score level 7 out of 10. All EDA features showed significant difference between painless and pain segments, termed for the 5-s segments before and after each pain stimulus. Random forest showed the highest accuracy in detecting pain, 81.5%, with 78.9% sensitivity and 84.2% specificity with leave-one-subject-out cross-validation approach. Our results show the potential of a smartphone application to provide near real-time objective pain detection.

scholarly journals Offline Algorithms in Low-Frequency Trading

Queue ◽  
2020 ◽  
Vol 18 (6) ◽  
pp. 37-51
Author(s):  
Terence Kelly
Keyword(s):  
Real Time ◽  
Real World ◽  
High Frequency ◽  
Low Frequency ◽  
Combinatorial Auctions ◽  
Hard Problem ◽  
Np Hard ◽  
High Stakes ◽  
Drill Bits ◽  
Np Hard Problem

Expectations run high for software that makes real-world decisions, particularly when money hangs in the balance. This third episode of the Drill Bits column shows how well-designed software can effectively create wealth by optimizing gains from trade in combinatorial auctions. We'll unveil a deep connection between auctions and a classic textbook problem, we'll see that clearing an auction resembles a high-stakes mutant Tetris, we'll learn to stop worrying and love an NP-hard problem that's far from intractable in practice, and we'll contrast the deliberative business of combinatorial auctions with the near-real-time hustle of high-frequency trading. The example software that accompanies this installment of Drill Bits implements two algorithms that clear combinatorial auctions.

Laparoscopic donor nephrectomy

2006 ◽  
Vol 21 (4) ◽  
pp. 521-526 ◽  
Author(s):  
Edward H. Chin ◽  
David Hazzan ◽  
Daniel M. Herron ◽  
John N. Gaetano ◽  
Scott A. Ames ◽  
...  
Keyword(s):  
Donor Nephrectomy ◽  
Laparoscopic Donor Nephrectomy
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