Regret Rate Research Articles

Ethical Need for a Fertility Decision-Aid for Transgender Adults of Reproductive Age

Ethical Need for a Fertility Decision-Aid for Transgender Adults of Reproductive Age

DISTRESS AND CAREER REGRET IN AUSTRALIAN ORTHOPAEDIC SURGICAL TRAINEES: A NATIONAL SURVEY

Physician health is a global concern, with increasing research efforts directed towards the challenge. Australia has limited published specialty-specific well-being data for trainees and consultants in medicine and surgery. We measured distress in Australian Orthopaedic trainees using the Physician Well-Being Index (PWBI, MedEd Web Solutions) using an online anonymous survey sent by the Australian Orthopaedic Association.The survey response rate was 38% (88/230). Forty-four percent of survey respondents met criteria for distress. Self-reported burnout in the 30 days prior was reported by 63% of respondents. Fifty-eight percent of females and 41% of males met criteria for distress. Of the 19% or respondents identifying as an ethnic minority, 53% were distressed compared to 42% of those identifying as non-ethnic minority. Trainees without a mentor had a 50% distress rate compared to those with a mentor (37% distress rate). Twenty-five percent of all trainees wished they had picked a career outside of medicine and 16% wished they had pursued a medical career other than Orthopaedic Surgery. Of those trainees who had already passed the fellowship exam, 17% wished they had pursued a career outside of medicine and 21% wished they had pursued a medical career in an area other than Orthopaedic Surgery.These findings suggest concerning rates of career regret and gender-related trends in distress in Australian Orthopaedic trainees. Females may be over-represented in our results as 17% of the source population was female compared to 22% of respondents. Further research is required across all Australian specialties to gain further understanding of factors contributing to distress and to assist in the development of strategy to protect against physician burnout.

Vasectomy Regret Among Childless Men

To address historical concerns surrounding vasectomy in childless men, we sought to evaluate for the level of regret in this unique cohort. The records of patients who underwent vasectomy via single surgeon between 2006 and 2021 were retrospectively reviewed and those who had not fathered children in any capacity at time of vasectomy were selected. We devised a 6-question survey inquiring about regret and thoughts on vasectomy reversal and assisted reproductive technology (ART). The questions are listed in Table 1. Patients were queried via a telephone call to rate their level of regret, both immediately after vasectomy and present day. The cohort was analyzed via age at time of vasectomy, time since vasectomy and marital status. There were 4812 overall patients who underwent vasectomy in this interval, with 205 (4.3%) who were childless. The response rate was 33.2% (68/205). Average age was 36.6 years with average time since vasectomy at time of phone call was 5.51 years. Regret rate was 4.4% immediately following vasectomy and 7.4% at time of telephone interview. A confirmatory, second consultation before vasectomy was present in 6.8% (14/205). The majority of patients 150/205 (73.1%) were married. When patients were stratified by marital status, there was no significant difference in any of the questions. The majority of patients were satisfied with their decision, with few contemplating or pursuing reversal or ART (Table 1). Regret in childless patients who undergo vasectomy is very rare, with the majority of patients feeling that their life was improved.

Adaptive Algorithm for Multi-Armed Bandit Problem with High-Dimensional Covariates

This article studies an important sequential decision making problem known as the multi-armed stochastic bandit problem with covariates. Under a linear bandit framework with high-dimensional covariates, we propose a general multi-stage arm allocation algorithm that integrates both arm elimination and randomized assignment strategies. By employing a class of high-dimensional regression methods for coefficient estimation, the proposed algorithm is shown to have near optimal finite-time regret performance under a new study scope that requires neither a margin condition nor a reward gap condition for competitive arms. Based on the synergistically verified benefit of the margin, our algorithm exhibits adaptive performance that automatically adapts to the margin and gap conditions, and attains optimal regret rates simultaneously for both study scopes, without or with the margin, up to a logarithmic factor. Besides the desirable regret performance, the proposed algorithm simultaneously generates useful coefficient estimation output for competitive arms and is shown to achieve both estimation consistency and variable selection consistency. Promising empirical performance is demonstrated through extensive simulation and two real data evaluation examples. Supplementary materials for this article are available online.

Information-gathering in latent bandits

In the latent bandit problem, the learner has access to reward distributions and – for the non-stationary variant – transition models of the environment. The reward distributions are conditioned on the arm and unknown latent states. The goal is to use the reward history to identify the latent state, allowing for the optimal choice of arms in the future. The latent bandit setting lends itself to many practical applications, such as recommender and decision support systems, where rich data allows the offline estimation of environment models with online learning remaining a critical component. Previous solutions in this setting always choose the highest reward arm according to the agent’s beliefs about the state, not explicitly considering the value of information-gathering arms. Such information-gathering arms do not necessarily provide the highest reward, thus may never be chosen by an agent that chooses the highest reward arms at all times.In this paper, we present a method for information-gathering in latent bandits. Given particular reward structures and transition matrices, we show that choosing the best arm given the agent’s beliefs about the states incurs higher regret. Furthermore, we show that by choosing arms carefully, we obtain an improved estimation of the state distribution, and thus lower the cumulative regret through better arm choices in the future. Through theoretical analysis we show that the proposed method retains the sub-linear regret rate of previous methods while having much better problem dependent constants. We evaluate our method on both synthetic and real-world data sets, showing significant improvement in regret over state-of-the-art methods.

Comparison of long-term results of obliterative colpocleisis and reconstructive vaginal surgery including sacrospinous ligament fixation in patients with total genital prolapse.

Treatment of total genital prolapse in elderly patients is still controversial in terms of postoperative objective and subjective results. The present study aimed to compare the long-term objective and subjective cure rates of sacrospinous ligament fixation and Le Fort operation for treatment of total genital prolapse. Patients over the age of 60 with stage 3 or 4 pelvic organ prolapse that presented to the Obstetrics and Gynaecology Clinic of the Faculty of Medicine of *** University. The study sample consisted of 17 patients that underwent Le Fort operation and 29 patients that underwent sacrospinous ligament fixation. Data on duration of operation, intraoperative complications, duration of hospital stay, and differences between preoperative and postoperative estimated blood loss, postoperative complications, and relapse in the long term were obtained. Questionnaires exploring quality of life, incontinence, and pelvic floor disorders were applied to the patients. As subjective cure rates, postoperative patient satisfaction (P = 0.001), regret rate (P = 0.038) and recommendation rate (P = 0.044), as well as postoperative questionnaire results, Pelvic Floor Impact Questionnaire and SF36 were found to be significantly better in the Le Fort group (respectively P = 0.039 and 0.042). As objective cure rates, there was no difference between the two groups in terms of postoperative cystocele, rectocele, and cystorectocele (P = 0.955) and postoperative recurrence of prolapse beyond the hymen (P: 0.893). Duration of operation and duration of hospital stay were found to be significantly shorter in the Le Fort group (respectively P = 0.032 and 0.012). Le Fort operation could be the intervention of choice in sexually inactive elderly patients with stage 3 or 4 pelvic organ prolapse.

Online Pricing with Offline Data: Phase Transition and Inverse Square Law

This paper investigates the impact of pre-existing offline data on online learning in the context of dynamic pricing. We study a single-product dynamic pricing problem over a selling horizon of T periods. The demand in each period is determined by the price of the product according to a linear demand model with unknown parameters. We assume that before the start of the selling horizon, the seller already has some pre-existing offline data. The offline data set contains n samples, each of which is an input-output pair consisting of a historical price and an associated demand observation. The seller wants to use both the pre-existing offline data and the sequentially revealed online data to minimize the regret of the online learning process. We characterize the joint effect of the size, location, and dispersion of the offline data on the optimal regret of the online learning process. Specifically, the size, location, and dispersion of the offline data are measured by the number of historical samples, the distance between the average historical price and the optimal price, and the standard deviation of the historical prices, respectively. For both single-historical-price setting and multiple-historical-price setting, we design a learning algorithm based on the “Optimism in the Face of Uncertainty” principle, which strikes a balance between exploration and exploitation and achieves the optimal regret up to a logarithmic factor. Our results reveal surprising transformations of the optimal regret rate with respect to the size of the offline data, which we refer to as phase transitions. In addition, our results demonstrate that the location and dispersion of the offline data also have an intrinsic effect on the optimal regret, and we quantify this effect via the inverse-square law. This paper was accepted by Omar Besbes, revenue management and market analytics. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2022.4322 .

Data-Driven Dynamic Pricing and Ordering with Perishable Inventory in a Changing Environment

We consider a retailer that sells a perishable product, making joint pricing and inventory ordering decisions over a finite time horizon of T periods with lost sales. Exploring a real-life data set from a leading supermarket chain, we identify several distinctive challenges faced by such a retailer that have not been jointly studied in the literature: the retailer does not have perfect information on (1) the demand-price relationship, (2) the demand noise distribution, (3) the inventory perishability rate, and (4) how the demand-price relationship changes over time. Furthermore, the demand noise distribution is nonparametric for some products but parametric for others. To tackle these challenges, we design two types of data-driven pricing and ordering (DDPO) policies for the cases of nonparametric and parametric noise distributions. Measuring performance by regret, that is, the profit loss caused by not knowing (1)–(4), we prove that the T-period regret of our DDPO policies are in the order of [Formula: see text] and [Formula: see text] in the cases of nonparametric and parametric noise distributions, respectively. These are the best achievable growth rates of regret in these settings (up to logarithmic terms). Implementing our policies in the context of the aforementioned real-life data set, we show that our approach significantly outperforms the historical decisions made by the supermarket chain. Moreover, we characterize parameter regimes that quantify the relative significance of the changing environment and product perishability. Finally, we extend our model to allow for age-dependent perishability and demand censoring and modify our policies to address these issues. This paper was accepted by David Simchi-Levi, Management Science Special Section on Data-Driven Prescriptive Analytics.

A Primal–Dual Learning Algorithm for Personalized Dynamic Pricing with an Inventory Constraint

We consider the problem of a firm seeking to use personalized pricing to sell an exogenously given stock of a product over a finite selling horizon to different consumer types. We assume that the type of an arriving consumer can be observed, but the demand function associated with each type is initially unknown. The firm sets personalized prices dynamically for each type and attempts to maximize the revenue over the season. We provide a learning algorithm that is near optimal when the demand and capacity scale in proportion. The algorithm utilizes the primal–dual formulation of the problem and learns the dual optimal solution explicitly. It allows the algorithm to overcome the curse of dimensionality (the rate of regret is independent of the number of types) and sheds light on novel algorithmic designs for learning problems with resource constraints.

Smooth Contextual Bandits: Bridging the Parametric and Nondifferentiable Regret Regimes

Dynamic Personalized Decision Making Beyond the Super-Extrapolatable and Super-Local Cases Contextual bandit problems model the inherent trade-off between exploration and exploitation in personalized decision making in marketing, healthcare, revenue management, and more. Specifically, the trade-off is characterized by the optimal growth rate of the regret. Intuitively, the optimal rate should depend on how complex the underlying supervised learning problem is, namely, how much can observing reward in one context tell us about mean rewards in another. To formalize this intuitive relationship, Hu, Kallus, and Mao study in “Smooth Contextual Bandits: Bridging the Parametric and Nondifferentiable Regimes” a nonparametric contextual bandit problem in which mean reward functions are β-times differentiable (more generally, Hölder β-smooth). This interpolates between two extremes previously studied in isolation: nondifferentiable bandits (β ≤ 1), with which running separated noncontextual bandits in different context regions achieves rate-optimal regret, and parametric-response bandits (β = ∞), with which rate-optimal regret can be achieved with minimal or no exploration because of infinite extrapolatability across contexts. The authors develop a rate-optimal algorithm that operates neither fully locally nor fully globally, revealing the optimal regret rate in this in-between smooth setting and shedding light on the crucial interplay of functional complexity and regret in dynamic personalized decision making.

Guideline-discordant care among females undergoing groin hernia repair: the importance of sex as a biologic variable.

Females suffer higher rates of operative recurrence and chronic pain following groin hernia repair. Guidelines recommend minimally invasive (MIS) groin hernia repair as the preferred approach to reduce these adverse outcomes. It is unknown what proportion of females receive MIS hernia repair. Therefore, our goal was to investigate adoption of evidence-based practices in groin hernia repair using sex as a biological variable. Retrospective cohort study of adults undergoing elective groin hernia repair (2014-2019) within a statewide quality improvement collaborative. Primary outcome was surgical approach. Multivariable logistic regression was performed to analyze the likelihood of undergoing MIS hernia repair. Secondary outcomes were 30-day adjusted rates of clinical and patient-reported outcomes (PROs). PROs included regret to undergo surgery among patients who completed post-operative surveys. Among 23,723 patients, the majority (90.7%) were males. Compared to males, females less often underwent an MIS surgical approach (37.4% vs 45.1%, p < 0.0001). After adjustment for patient and clinical variables, females remained significantly less likely to undergo MIS groin hernia repair (aOR 0.88, 95% CI 0.80-0.97). Adjusted clinical outcomes were not different between males and females. Among 4325 patients who completed post-operative surveys, adjusted rates of regret to undergo surgery were higher among females (12.9% vs 8.5%, p = 0.003). Even after adjusting for differences, females were less likely to receive guideline-concordant groin hernia repair and were more likely to regret surgery. Understanding the behaviors of surgeons who treat females with groin hernia may inform quality metrics to promote best practices in this population.

Analytics for IoT‐Enabled Human–Robot Hybrid Sortation: An Online Optimization Approach

Motivated by a realworld practice of a China Post sortation center, this study considers the deployment of Internet of Things (IoT) technology to improve the efficiency of a human–robot hybrid sortation system. In this system, IoT technology enables responsive adjustment of the manual capacity to alleviate the congestion effect of increasing parcel flow on robotic sorting efficiency. We begin with a predictive analysis of a real‐life dataset to quantify the congestion effect on robotic sorting efficiency, and examine the nonstationary behavior of the parcel flow process. Subsequently, we design an online algorithm using IoT advance information (which refers to advance observation of some parcel flows before their actual arrivals) for efficiently adjusting the manual capacity. We develop theoretical guarantee for the effectiveness of the online algorithm by bounding its regret, and also demonstrate that the long‐term expected regret rate is zero under mild conditions. Via extensive simulation experiments, we find that our online algorithm outperforms the China Post sortation center's current policy and conventional Markov dynamic program in terms of computational efficiency and solution quality. The simulation results also demonstrate that the value of IoT technology to the sortation center can be significant, and shed insights on IoT investment by revealing the diminishing return of expanding the horizon of IoT advance information.

Is Bonesetter Still A Choice in the Eastern Region of Turkey? A Retrospective Study Investigating Bonesetter Interventions and Consequences

Objective: The aim of the current study is to evaluate the epidemiological and demographic characteristics of patients admitted to bonesetters and to determine the complications that occur in these patients. Materials and methods: Patients who applied to Elazig Fethi Sekin City Hospital due to musculoskeletal trauma between January 2018 and January 2021 and were admitted to a bonesetter before this admission were evaluated retrospectively. The patients’ age, gender, educational status, occupation, reason for applying to the bonesetter, the diagnosis told to the patient by the bonesetter, the actual diagnosis of the patient and the treatment given to the patient were evaluated, respectively. Complications were also evaluated by examining the patient’s physical examination and radiographic examinations. Results: The most common orthopedic disorder in patients was distal radius fracture and the most common reason for utilizing bonesetters was the recommendation of family and relatives. All of the diagnoses made by the bonesetters were defined as dislocations. There was no diagnosis of dislocation among the diagnoses made by physicians. In the student group; joint stiffness was found to be significantly lower than in the worker group. In the worker group; a significantly higher number of deformities were observed. More than half of the patients did not regret applying to a bonesetter. The rate of regret for men to apply to the bonesetters was found to be statistically significantly higher than that of women. Those who answered yes to the question of regret had significantly higher complications than those who answered no. Conclusion: Especially in the eastern regions of our country, the application to bonesetters has been one of the ongoing realities of our country for years. Healthcare professionals have a major role to play in reducing the rate of referrals to bonesetters.

Dynamic Pricing and Inventory Control with Fixed Ordering Cost and Incomplete Demand Information

We consider the periodic review dynamic pricing and inventory control problem with fixed ordering cost. Demand is random and price dependent, and unsatisfied demand is backlogged. With complete demand information, the celebrated [Formula: see text] policy is proved to be optimal, where s and S are the reorder point and order-up-to level for ordering strategy, and [Formula: see text], a function of on-hand inventory level, characterizes the pricing strategy. In this paper, we consider incomplete demand information and develop online learning algorithms whose average profit approaches that of the optimal [Formula: see text] with a tight [Formula: see text] regret rate. A number of salient features differentiate our work from the existing online learning researches in the operations management (OM) literature. First, computing the optimal [Formula: see text] policy requires solving a dynamic programming (DP) over multiple periods involving unknown quantities, which is different from the majority of learning problems in OM that only require solving single-period optimization questions. It is hence challenging to establish stability results through DP recursions, which we accomplish by proving uniform convergence of the profit-to-go function. The necessity of analyzing action-dependent state transition over multiple periods resembles the reinforcement learning question, considerably more difficult than existing bandit learning algorithms. Second, the pricing function [Formula: see text] is of infinite dimension, and approaching it is much more challenging than approaching a finite number of parameters as seen in existing researches. The demand-price relationship is estimated based on upper confidence bound, but the confidence interval cannot be explicitly calculated due to the complexity of the DP recursion. Finally, because of the multiperiod nature of [Formula: see text] policies the actual distribution of the randomness in demand plays an important role in determining the optimal pricing strategy [Formula: see text], which is unknown to the learner a priori. In this paper, the demand randomness is approximated by an empirical distribution constructed using dependent samples, and a novel Wasserstein metric-based argument is employed to prove convergence of the empirical distribution. This paper was accepted by J. George Shanthikumar, big data analytics.

Association of Treatment Modality, Functional Outcomes, and Baseline Characteristics With Treatment-Related Regret Among Men With Localized Prostate Cancer

Treatment-related regret is an integrative, patient-centered measure that accounts for morbidity, oncologic outcomes, and anxiety associated with prostate cancer diagnosis and treatment. To assess the association between treatment approach, functional outcomes, and patient expectations and treatment-related regret among patients with localized prostate cancer. This population-based, prospective cohort study used 5 Surveillance, Epidemiology, and End Results (SEER)-based registries in the Comparative Effectiveness Analysis of Surgery and Radiation cohort. Participants included men with clinically localized prostate cancer from January 1, 2011, to December 31, 2012. Data were analyzed from August 2, 2020, to March 1, 2021. Prostate cancer treatments included surgery, radiotherapy, and active surveillance. Patient-reported treatment-related regret using validated metrics. Regression models were adjusted for demographic and clinicopathologic characteristics, treatment approach, and patient-reported functional outcomes. Among the 2072 men included in the analysis (median age, 64 [IQR, 59-69] years), treatment-related regret at 5 years after diagnosis was reported in 183 patients (16%) undergoing surgery, 76 (11%) undergoing radiotherapy, and 20 (7%) undergoing active surveillance. Compared with active surveillance and adjusting for baseline differences, active treatment was associated with an increased likelihood of regret for those undergoing surgery (adjusted odds ratio [aOR], 2.40 [95% CI, 1.44-4.01]) but not radiotherapy (aOR, 1.53 [95% CI, 0.88-2.66]). When mediation by patient-reported functional outcomes was considered, treatment modality was not independently associated with regret. Sexual dysfunction, but not other patient-reported functional outcomes, was significantly associated with regret (aOR for change in sexual function from baseline, 0.65 [95% CI, 0.52-0.81]). Subjective patient-perceived treatment efficacy (aOR, 5.40 [95% CI, 2.15-13.56]) and adverse effects (aOR, 5.83 [95% CI, 3.97-8.58]), compared with patient expectations before treatment, were associated with treatment-related regret. Other patient characteristics at the time of treatment decision-making, including participatory decision-making tool scores (aOR, 0.80 [95% CI, 0.69-0.92]), social support (aOR, 0.78 [95% CI, 0.67-0.90]), and age (aOR, 0.78 [95% CI, 0.62-0.97]), were significantly associated with regret. Results were comparable when assessing regret at 3 years rather than 5 years. The findings of this cohort study suggest that more than 1 in 10 patients with localized prostate cancer experience treatment-related regret. The rates of regret appear to differ between treatment approaches in a manner that is mediated by functional outcomes and patient expectations. Treatment preparedness that focuses on expectations and treatment toxicity and is delivered in the context of shared decision-making should be the subject of future research to examine whether it can reduce regret.

A regret lower bound for assortment optimization under the capacitated MNL model with arbitrary revenue parameters

AbstractIn this note, we consider dynamic assortment optimization with incomplete information under the capacitated multinomial logit choice model. Recently, it has been shown that the regret (the cumulative expected revenue loss caused by offering suboptimal assortments) that any decision policy endures is bounded from below by a constant times $\sqrt {NT}$, where $N$ denotes the number of products and $T$ denotes the time horizon. This result is shown under the assumption that the product revenues are constant, and thus leaves the question open whether a lower regret rate can be achieved for nonconstant revenue parameters. In this note, we show that this is not the case: we show that, for any vector of product revenues there is a positive constant such that the regret of any policy is bounded from below by this constant times $\sqrt {N T}$. Our result implies that policies that achieve ${{\mathcal {O}}}(\sqrt {NT})$ regret are asymptotically optimal for all product revenue parameters.

MP46-18 PATIENTS REPORT SATISFACTION/REGRET FOLLOWING FOCAL THERAPY FOR LOCALIZED PROSTATE CANCER: A PROSPECTIVE MULTICENTER EVALUATION

You have accessJournal of UrologyProstate Cancer: Localized: Ablative Therapy (MP46)1 Sep 2021MP46-18 PATIENTS REPORT SATISFACTION/REGRET FOLLOWING FOCAL THERAPY FOR LOCALIZED PROSTATE CANCER: A PROSPECTIVE MULTICENTER EVALUATION Alireza Ghoreifi, Masatomo Kaneko, Samuel Peretsman, Atsuko Iwata, Aliasger Shakir, Dordaneh Sugano, Jie Cai, Giovanni Cacciamani, Daniel Park, Osamu Ukimura, Duke Bahn, Inderbir Gill, and Andre Luis Abreu Alireza GhoreifiAlireza Ghoreifi More articles by this author , Masatomo KanekoMasatomo Kaneko More articles by this author , Samuel PeretsmanSamuel Peretsman More articles by this author , Atsuko IwataAtsuko Iwata More articles by this author , Aliasger ShakirAliasger Shakir More articles by this author , Dordaneh SuganoDordaneh Sugano More articles by this author , Jie CaiJie Cai More articles by this author , Giovanni CacciamaniGiovanni Cacciamani More articles by this author , Daniel ParkDaniel Park More articles by this author , Osamu UkimuraOsamu Ukimura More articles by this author , Duke BahnDuke Bahn More articles by this author , Inderbir GillInderbir Gill More articles by this author , and Andre Luis AbreuAndre Luis Abreu More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002067.18AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The objective is to evaluate the treatment decision satisfaction and regret among patients who underwent Focal Therapy (FT) for localized prostate cancer (PCa) with high-intensity focused ultrasound (HIFU) or cryoablation (CRYO). METHODS: We identified consecutive patients who underwent hemi-gland HIFU or CRYO FT as primary treatment for localized PCa at three US institutions (IRB# HS-17-00749). A survey with validated questionnaires was mailed between February and December of 2020, including: 5-question Decision Regret Scale (DRS); International Prostate Symptom Score (IPSS); and International Index of Erectile Function (IIEF-5). Potency was defined as IIEF-5 ≥ 18. The regret score was calculated based on the 5 items of DRS (Figure). Agreement with each item is measured on a 5-point scale and the final score is converted to a 0-100 scale. Regret was defined as DRS ≥ 26. Multivariable logistic regression models were applied to assess baseline and post-FT predictors for treatment decision regret. Statistically significant if p<0.05. RESULTS: Out of 236 patients, 143 (61%) responded the survey (71/116 HIFU and 72/120 CRYO). Baseline characteristics were similar between responders and no-responders. For the responders, the median (IQR) follow-up was 43 (26-68) months. The treatment decision regret rate was 19.6%, being CRYO 23.6% vs. HIFU 15.5% (OR 1.7, p=0.22). The Figure shows the details of patients’ responses to each DRS question. Higher PSA at nadir (OR 1.48, 95%CI 1.1-2, p=0.009) post FT, presence of PCa on the follow-up biopsy (OR 3.98, 95%CI 1.5-10.6, p=0.006), higher IPSS after FT (OR 1.18, 95%CI 1.01-1.04, p=0.03), and de-novo impotence (OR 6.67, 95%CI 1.57-27, p=0.03) were independent predictors of treatment decision regret. CONCLUSIONS: Focal therapy for localized PCa is well-accepted by the patients. Higher PSA at nadir, presence of cancer on the follow-up biopsy, higher post-operative IPSS, and de-novo impotence are independent predictors of treatment decision regret. Source of Funding: None © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e820-e821 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Alireza Ghoreifi More articles by this author Masatomo Kaneko More articles by this author Samuel Peretsman More articles by this author Atsuko Iwata More articles by this author Aliasger Shakir More articles by this author Dordaneh Sugano More articles by this author Jie Cai More articles by this author Giovanni Cacciamani More articles by this author Daniel Park More articles by this author Osamu Ukimura More articles by this author Duke Bahn More articles by this author Inderbir Gill More articles by this author Andre Luis Abreu More articles by this author Expand All Advertisement Loading ...

Follow-up of patients after colpectomy or Le Fort colpocleisis: Single center experience

ObjectiveAs the population ages, obliterative procedures (OP) are expected to become an increasingly important treatment option. The primary aim of this study was to evaluate vaginal and urinary symptoms 3 months after OP and peri- and postoperative complications. The secondary aim was to investigate long-term outcomes, including patient satisfaction, regret rate and the rate of symptomatic recurrent prolapse after OP. Another secondary aim was to evaluate the feasibility of performing OP under local anesthesia (LA) as increasingly more operations are performed under LA. Study designRetrospective study of 43 women who underwent OP during a 10-year period. Patients completed three prolapse questions from the International Consultation on Incontinence-Vaginal Symptoms (ICIQ-VS) and the International Consultation on Incontinence Questionnaire- Urinary Incontinence Short Form (ICIQ-UI SF) before undergoing surgery, 3 months postoperatively and at long-term follow-up. Records were reviewed for complications, use of anesthetics, recurrences, patient satisfaction and regret. ResultsA Le Fort colpocleisis was performed in 31 (72 %) and a colpectomy in 12 (28 %) patients. At 3 months` follow-up, patients had a statistically significant improvement in vaginal symptoms. Twelve patients (46 %) became continent, compared with 14 (54 %) with remaining urinary incontinence (UI). There were no patients with de novo UI 3 months’ after surgery. Total complication rate was 4,6% (2/43). A symptomatic recurrent prolapse occurred in 4 patients (9.3 %). The satisfaction rate was 86 %. No patients reported regret choosing to have vaginal closure surgery. Twenty one (49 %) of the procedures were performed under local anesthesia with intravenous sedation. ConclusionObliterative procedures are good surgical options for elderly women with a positive impact on vaginal and urinary symptoms, low complication and recurrence rate. Patients report high satisfaction and no regret over loss of sexual ability at longterm follow-up. OP under LA with intravenous sedation is a feasible and safe option.

Complications and Patient-reported Outcomes in Transfemale Vaginoplasty: An Updated Systematic Review and Meta-analysis

A update on our previous systematic review was conducted. Several databases including MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Scopus were assessed. Random effects meta-analysis and subgroup analyses were performed. After compiling the results of the update with the previous systematic review, a total of 57 studies pooling 4680 cases were included in the systematic review, and 52 studies were used in the meta-analysis. Overall pooled data including any surgical technique showed rates of 1% [95% confidence interval (CI) <0.1%-2%] of fistula, 11% (95% CI 8%-14%) of stenosis and/or strictures, 4% (95% CI 1%-9%) of tissue necrosis, and 3% (95% CI 1%-4%) of prolapse. Overall satisfaction was 91% (81%-98%). Regret rate was 2% (95% CI <1%-3%). Average neovaginal depth was 9.4 cm (7.9-10.9 cm) for the penile skin inversion and 15.3 cm (13.8-16.7 cm) for the intestinal vaginoplasty. Transfemale vaginoplasty is a key component of the comprehensive surgical treatment of transfemale patients with gender dysphoria. Over time, we will see an increased demand for these procedures, so adequate surgical training, clinical/surgical experience, and research outcomes are required, as we continue to strive to provide the best care possible for a population in need.

Nonparametric Learning Algorithms for Joint Pricing and Inventory Control with Lost Sales and Censored Demand

We consider a joint pricing and inventory control problem in which the customer’s response to selling price and the demand distribution are not known a priori. Unsatisfied demand is lost and unobserved, and the only available information for decision making is the observed sales data (also known as censored demand). Conventional approaches, such as stochastic approximation, online convex optimization, and continuum-armed bandit algorithms, cannot be employed, because neither the realized values of the profit function nor its derivatives are known. A major challenge of this problem lies in that the estimated profit function constructed from observed sales data is multimodal in price. We develop a nonparametric spline approximation–based learning algorithm. The algorithm separates the planning horizon into a disjoint exploration phase and an exploitation phase. During the exploration phase, a spline approximation of the demand-price function is constructed based on sales data, and then the corresponding surrogate optimization problem is solved on a sparse grid to obtain a pair of recommended price and target inventory level. During the exploitation phase, the algorithm implements the recommended strategies. We establish a (nearly) square-root regret rate, which (almost) matches the theoretical lower bound.