@article{ChanTCY.J139,

title = {A comparison of lower extremity squat, lunge, and hip hinge kinematics between marker based and markerless motion capture systems},

author = {K. Liu and S. Hirsch and P. Singh and T. C. Y. Chan and T. A. Burkhart and M. G. Hutchison},

doi = {10.1177/17543371251348697},

year  = {2026},

date = {2026-01-06},

urldate = {2026-01-06},

journal = {Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology},

abstract = {The resource-intensive nature of traditional marker-based motion capture systems limits opportunities for quantitative motion analysis. However, the advancement of markerless motion capture technology yields tremendous promise for accessible kinematic analyses beyond conventional research settings. This work compared the lower limb kinematics measured by a neural network-driven markerless motion capture system to those from a standard marker-based motion capture system during squat, hip hinge, and reverse lunge tasks. Fourteen adults performed three repetitions of each movement while being recorded simultaneously by two iPads and 17 infrared optical motion capture cameras. The mean peak cross-correlation values indicated high agreement for knee and hip flexion ( . 0.95) and poor agreement for hip adduction, knee varus, and internal rotation (  0.49) for all tasks; agreement for hip internal rotation and ankle flexion was task dependent (0.27–0.97). The average root mean square error indicated joint-specific offsets between systems, as values ranged from 6.43° to 12.32° for the knee, 11.25° to 17.35° for the hip, and 21.51° to 25.67° for the ankle. These findings suggest that, while the markerless system demonstrates the ability to capture gross motor patterns in the sagittal plane, further refinement of the underlying models is needed to improve the validity of the system’s measurements.},

keywords = {},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{ChanTCY.J138,

title = {Income pools for superstar markets},

author = {T. C. Y. Chan and N. Chen and C. Fernandes},

doi = {10.1287/mnsc.2024.07480},

year  = {2026},

date = {2026-01-05},

urldate = {2026-01-05},

journal = {Management Science},

volume = {72},

number = {4},

pages = {2807-2825},

abstract = {“Superstar” markets, characterized by a small portion of individuals earning disproportionately large salaries compared to their peers, have been identified in industries such as entrepreneurship, sports, music and entertainment. Individuals entering these markets face extreme income uncertainty which may deter some of them from entering altogether or force them to exit early. To address this difficulty, we propose income pools. An income pool involves individuals agreeing that if any one of them reaches a particular salary milestone, a portion of their future earnings will be shared amongst all members in their pool. Despite growing interest and implementation of income pools in practice, they have not been studied yet in the academic literature. In response, we develop the first mathematical model to analyze income pools, focusing on stability (i.e., pools where no agents leave or join). We concentrate on risk-averse agents and show that they prefer to join income pools, but no finite-sized stable pool exists. There are two remedies to this instability. First, by introducing an upper bound on pool size, we show that a bounded stable pool always exists and is easily identifiable. In general, these pools require more “weaker” (i.e., lower chances of success) agents than strong ones to remain stable. Interestingly, we prove a “Pareto dominance” result, whereby all agents in a given bounded stable pool will simultaneously prefer a unique bounded stable pool. Second, by introducing a cost ϵ to leave/join a pool, we show that finite-sized ϵ-stable pools always exist and provide a sufficient condition to identify them. We analyze two extensions, individualized contracts and single-winner-take-all markets, and show that the instability result persists in both scenarios. We conclude with a case study using real professional baseball player data that demonstrates a 20%-30% increase in social welfare if players join income pools, under varying income pool contract parameters. This increase is highest for players with the lowest probability of reaching superstardom. We present practical implications of our theoretical and numerical results in terms of creating finite-sized pools.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J137,

title = {The impact of the COVID-19 pandemic on bystander CPR and AED rates in Canada},

author = {I. E. Blanchard and E. Ghamarian and J. Zotzman and K. N. Dainty and A. Cournoyer and F. Alnaji and T. C. Y. Chan and S. Cheskes and S. Lin and S. van Diepen and M. Austin and S. Leduc and M. Welsford and R. Mohindra and F. de Champlain and M. Davis and J. P. Nicholson and C. D. G. Keown-Stoneman and C. Truong and I. R. Drennan and B. Grunau},

doi = {10.1016/j.resplu.2025.101118},

year  = {2025},

date = {2025-09-29},

urldate = {2025-09-29},

journal = {Resuscitation Plus},

volume = {26},

pages = {101118},

abstract = {Objective

To evaluate whether the COVID-19 pandemic was associated with changes in bystander CPR and automated external defibrillator (AED) application in Canada.



Methods

We included adult emergency medical services (EMS)-treated out-of-hospital cardiac arrests (OHCAs) from the Canadian national cardiac arrest registry. The outcomes were bystander CPR and AED application. We fit adjusted piecewise linear segmented logistic regression models to estimate whether the peri-COVID period (February 2020-December 2021), in comparison to pre-COVID (January 2018-January 2020), was associated with a change in the odds of bystander CPR and AED application. We also examined subgroups of private and public only OHCAs.



Results

Among the 24,410 OHCAs, the median age was 65 years (IQR 50,77), with 7,822 (32%) females. In the pre-COVID (n=11,271) and peri-COVID (n=13,139) periods, 6,244 (55%) and 7,924 (60%) cases received bystander CPR (+4.9% difference, 95% CI 3.7, 6.2), and 502 (4.5%) and 432 (3.3%) were treated with bystander AEDs (-1.2% difference, 95% CI -1.7, -0.68) respectively. The peri-COVID period was associated with an increased odds of bystander CPR (aOR 1.15; 95% CI 1.03, 1.27) and a decreased odds of bystander AED application (aOR 0.65; 95% CI 0.48, 0.86). This appears to be driven by increases in private-setting bystander CPR (aOR 1.19; 95% CI 1.06, 1.33) and decreases in public-setting AED use (aOR 0.59; 95% CI 0.40, 0.88).



Conclusions

The COVID-19 pandemic was associated with an increase in bystander CPR and a decrease in bystander AED application.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J136,

title = {Machine learning-augmented optimization of large bilevel and two-stage stochastic programs: Application to cycling network design},

author = {T. C. Y. Chan and B. Lin and S. Saxe},

doi = {10.1287/msom.2024.1317},

year  = {2025},

date = {2025-08-21},

urldate = {2025-08-21},

journal = {Manufacturing & Service Operations Management},

volume = {27},

number = {6},

pages = {1851-1868},

abstract = {Problem definition: A wide range of decision problems can be formulated as bilevel programs with independent followers, which, as a special case, include two-stage stochastic programs. These problems are notoriously difficult to solve, especially when a large number of followers are present. Motivated by a real-world cycling infrastructure planning application, we present a general approach to solving such problems. 

Methodology/results:

We propose an optimization model that explicitly considers a sampled subset of followers and exploits a machine learning model to estimate the objective values of unsampled followers. We prove bounds on the optimality gap of the generated leader decision as measured by the original objective function that considers the full follower set. We then develop follower sampling algorithms to tighten the bounds and a representation learning approach to learn follower features, which are used as inputs to the embedded machine learning model. Through numerical studies, we show that our approach generates leader decisions of higher quality compared with baselines. Finally, in collaboration with the City of Toronto, we perform a real-world case study in Toronto, where we solve a cycling network design problem with over one million followers. Compared with the current practice, our approach improves Toronto’s cycling accessibility by 19.2%, equivalent to $18 million in potential cost savings. 

Managerial implications: Our approach is being used to inform the cycling infrastructure planning in Toronto and can be generalized to any decision problems that are formulated as bilevel programs with independent followers.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J135,

title = {Feasibility and optimization of a second-tier prehospital critical care response for major trauma in a North American urban and suburban area: A geospatial analysis and modelling study},

author = {R. Stephenson and V. Sarhangian and S. Cheskes and L. Turner and B. Nolan and I. Drennan and T. C. Y. Chan and J. von Vopelius-Feldt},

doi = {10.1016/j.ajem.2025.07.025},

year  = {2025},

date = {2025-07-09},

urldate = {2025-07-09},

journal = {American Journal of Emergency Medicine},

volume = {97},

pages = {35-44},

abstract = {Objective

Prehospital Critical Care Response Units (CCRUs) dispatched to the scene of major traumas can deliver advanced interventions at scene but are uncommon in North America. We sought to evaluate the feasibility of CCRU response to major trauma in a North American urban-suburban region.



Methods

We obtained ambulance record-level data from three paramedic services in Ontario, Canada (Toronto Paramedic Service, Peel Regional Paramedic Service, and Halton Region Paramedic Service) from January 2018 to December 2022 which we aggregated into calls and applied inclusion criteria targeting major trauma. We used mathematical modelling to determine the optimal placement of CCRU bases containing an RRV or RRV/helicopter for trauma response and evaluated their expected counterfactual coverage performance using simulation. Our primary metrics were the expected number of major traumas that could have been reached by CCRUs prior to EMS departure from the scene and the resulting expected average reduction in time to accessing critical care for those patients.



Results

We found the expected counterfactual coverage of two optimally placed RRV teams to be 80 % (N = 5092) of 6391 major trauma calls included. This corresponded to an expected average reduction in time to critical care of 30 min (from 47 to 17 min). We found only marginal improvement in total calls reached by CCRUs when an RRV team was replaced with an RRV/helicopter team.



Conclusions

Our analysis supports the feasibility of CCRU response to major trauma in a North American mixed urban-suburban region and motivates further investigation into CCRUs' clinical and cost effectiveness.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J134,

title = {Development of a patient reported outcomes based machine learning model to predict recurrences in head and neck cancer},

author = {C. M. K. L. Yao and K. Hueniken and S. H. Huang and G. Liu and S. Bratman and A. Hope and A. McPartlin and J. C. Tsai and S. Tzelnik and D. Goldstein and A. Hosni and T. C. Y. Chan and J. R. de Almeida},

doi = {10.1016/j.oraloncology.2025.107304},

year  = {2025},

date = {2025-04-13},

urldate = {2025-04-13},

journal = {Oral Oncology},

volume = {165},

pages = {107304},

abstract = {Introduction: Recurrence rates among Head and Neck Cancer (HNC) patients are high, with earlier detection associated with improved survival. Patient-reported outcomes (PROs) have increasingly been found to predict patient care needs. Here, we examine whether PROs specific to HNC patients or general can predict disease progression using Machine Learning (ML) algorithms.



Methods: This was an analysis of 1,302 HNC patients, including patients who completed at least one MD Anderson Symptom Inventory (MDASI) or Edmonton Symptom Assessment Score (ESAS) questionnaire 3 months following curative intent treatment. ML models, including least absolute shrinkage and selection operator (LASSO) logistic regression and Random Forest (RF) were applied to baseline or longitudinal PRO changes to predict recurrences. Predictive performances were assessed via area under the receiver-operating curve, computed with 10-fold cross-validation. Relative variable importance were computed with average decrease in out-of-bag prediction accuracy of each tree.



Results: Disease recurrence occurred in 9.5 % (n = 123) of HNC patients. Baseline post-treatment MDASI, RF models demonstrated an area under the curve (AUC) approximating 0.675, sensitivity of 0.83 and specificity of 0.58 with pain, speech, and dry mouth as key variables. When stratifying patients by HPV status, our non-HPV model based on pain, distress, and mood yielded an AUC of 0.71 at 3 months and 0.70 at 6 months.



Conclusion: ML models using HNC specific PROs can identify patients at high risk for disease progression with moderate accuracy. Prospective studies with larger dataset and further analysis are needed to refine these models and evaluate their potential in guiding post-treatment surveillance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J133,

title = {Cost-effectiveness of drone-delivered automated external defibrillators for out-of-hospital cardiac arrest},

author = {M. Maaz and K. H. B. Leung and J. J. Boutilier and S. Suen and P. Dorian and L. J. Morrison and D. Scales and S. Cheskes and T. C. Y. Chan},

doi = {10.1016/j.resuscitation.2025.110552},

year  = {2025},

date = {2025-02-13},

urldate = {2025-02-13},

journal = {Resuscitation},

volume = {209},

pages = {110552},

abstract = {Background: Out-of-hospital cardiac arrest (OHCA) is a significant cause of mortality and morbidity in North America, for which timely defibrillation of shockable rhythms is essential. Drones have been proposed as an intervention to improve response time and are being implemented in practice.



Aim: To determine the cost-effectiveness of drone-delivered automated external defibrillators (AEDs) for OHCAs.



Methods: Using data from 22,017 OHCAs in Ontario, Canada over 10 years, we developed a comprehensive computational framework combining machine learning, optimization and a Markov microsimulation model to provide an economic evaluation of 964 different drone networks across a wide range of sizes and configurations. We simulated response times, survival outcomes, lifetime quality-adjusted life-years (QALYs), lifetime healthcare costs, and 10-year operational costs for each network.



Results: All 964 drone networks were cost-effective. We identified 20 networks on the cost-QALY efficient frontier, each with shorter response times, more survivors across all categories, and higher costs per survivor. Historical ambulance response (i.e., standard care) had mean response time of 6 min 21 s. On the efficient frontier, average drone response times were 32% to 71% shorter than standard care. There were 1,855 (8.4%) survivors to hospital discharge in standard care, which increased by 21% to 46% across the 20 drone networks. The smallest non-dominated drone network, with 20 drones, cost $20,912 per QALY gained. All drone networks had higher net monetary benefit than standard care. Cost-effectiveness was even greater for shockable and witnessed populations. Extensive sensitivity analyses showed that our results were robust to changes in modelling assumptions.



Conclusions: Drone-delivered AEDs were associated with reductions in response time, mortality and morbidity, and were found to be highly cost-effective relative to standard ambulance response with no drones.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J132,

title = {Optimizing placement of public-access naloxone kits using geospatial analytics: A modelling study},

author = {K. H. B. Leung and B. E. Grunau and M. K. Lee and J. A. Buxton and J. Helmer and S. van Diepen and J. Christenson and T. C. Y. Chan},

doi = {10.1503/cmaj.241228},

year  = {2025},

date = {2025-02-05},

urldate = {2025-02-05},

journal = {CMAJ},

volume = {197},

number = {10},

pages = {E258-E265},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J131,

title = {Impact of surgeon-radiation oncology dyads in oral cavity cancer outcomes},

author = {J. Wihlidal and A. O. Esemezie and S. H. Huang and E. Watson and R. W. Gilbert and J. Waldron and P. J. Gullane and A. Hope and J. C. Irish and B. O’Sullivan and D. B. Chepeha and J. J. H. Kim and D. Brown and B. C. J. Cho and I. J. Witterick and E. Monteiro and J. C. Davies and J. Ringash and D. P. Goldstein and S. Bratman and A. Bayley and J. R. de Almeida and T. C. Y. Chan and A. Hosni and C. M. K. L. Yao},

doi = {10.1245/s10434-024-16385-4},

year  = {2025},

date = {2025-01-12},

urldate = {2025-01-12},

journal = {Annals of Surgical Oncology},

volume = {32},

number = {2},

pages = {1270-1279},

abstract = {Background. Multidisciplinary care is paramount in patient-specific decision making, especially as pertaining to oral cavity squamous cell cancer (OCSCC) treatment. Protracted surgery-postoperative-radiation (S-PORT) has a detrimental impact on OCSCC patients’ outcomes. This study examined the impact of surgeon-radiation oncologist dyads on the treatment of OCSCC, focusing on S-PORT interval and disease specific outcomes.



Methods. All OCSCC patients treated in a tertiary cancer center between 2009 to 2017 were included. Patients were categorized into “dyad” and “nondyad” groups defined as whether they were treated by a paired surgeon–radiation oncology team with joint multidisciplinary clinic or shared >30% patient volumes. Univariate and multivariate logistic regression were performed to identify factors associated with a prolonged S-PORT time interval (≥8 weeks). Overall survival and locoregional recurrence were estimated and compared.



Results. A total of 444 OCSCC were eligible. Treatment by a dyad was significantly less likely associated with S-PORT ≥ 8 weeks (odds ratio ­ [OR]unadjusted: 0.65; 95% confidence interval [CI] 0.44–0.96; p = 0.03). Obtaining pre-operative radiation oncology consultation also decreased the S-PORT interval. Advanced T-category and the need for free tissue flap reconstruction increased the likelihood of prolonged S-PORT on univariate but not multivariate analysis. No significant differences were observed in overall survival or locoregional recurrence by dyad status nor S-PORT (p > 0.05).



Conclusions. Surgeon-radiation oncology dyads significantly minimized time from surgery to postoperative radiation in OCSCC. While improvement in overall survival or locoregional recurrence was not observed, these findings support close knit collaborative multidisciplinary treatment care models, including dyad-based care.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}