Quantitative Prediction of Vertical Ionization Potentials from DFT via a Graph-Network-Based Delta Machine Learning Model Incorporating Electronic Descriptors.

While accurate wave function theories like CCSD(T) are capable of modeling molecular chemical processes, the associated steep computational scaling renders them intractable for treating large systems or extensive databases. In contrast, density functional theory (DFT) is much more computationally feasible yet often fails to quantitatively describe electronic changes in chemical processes. Herein, we report an efficient delta machine learning (ΔML) model that builds on the Connectivity-Based Hierarchy (CBH) scheme─an error correction approach based on systematic molecular fragmentation protocols─and achieves coupled cluster accuracy on vertical ionization potentials by correcting for deficiencies in DFT. The present study integrates concepts from molecular fragmentation, systematic error cancellation, and machine learning. First, we show that by using an electron population difference map, ionization sites within a molecule may be readily identified, and CBH correction schemes for ionization processes may be automated. As a central feature of our work, we employ a graph-based QM/ML model, which embeds atom-centered features describing CBH fragments into a computational graph to further increase accuracy for the prediction of vertical ionization potentials. In addition, we show that the incorporation of electronic descriptors from DFT, namely electron population difference features, improves model performance well beyond chemical accuracy (1 kcal/mol) to approach benchmark accuracy. While the raw DFT results are strongly dependent on the underlying functional used, for our best models, the performance is robust and much less dependent on the functional.

A Fragmentation-Based Graph Embedding Framework for QM/ML.

We introduce a new fragmentation-based molecular representation framework "FragGraph" for QM/ML methods involving embedding fragment-wise fingerprints onto molecular graphs. Our model is specifically designed for delta machine learning (Δ-ML) with the central goal of correcting the deficiencies of approximate methods such as DFT to achieve high accuracy. Our framework is based on a judicious combination of ideas from fragmentation, error cancellation, and a state-of-the-art deep learning architecture. Broadly, we develop a general graph-network framework for molecular machine learning by incorporating the inherent advantages prebuilt into error cancellation methods such as the generalized Connectivity-Based Hierarchy. More specifically, we develop a QM/ML representation through a fragmentation-based attributed graph representation encoded with fragment-wise molecular fingerprints. The utility of our representation is demonstrated through a graph network fingerprint encoder in which a global fingerprint is generated through message passing of local neighborhoods of fragment-wise fingerprints, effectively augmenting standard fingerprints to also include the inbuilt molecular graph structure. On the 130k-GDB9 dataset, our method predicts an out-of-sample mean absolute error significantly lower than 1 kJ/mol compared to target G4(MP2) calculated energies, rivaling current deep learning methods with reduced computational scaling.

The effects of augmenting clozapine with oxytocin in schizophrenia: An initial case series.

BACKGROUND: Debilitating symptoms of schizophrenia often persist after sustained treatment with atypical antipsychotics. To date, clozapine has been the most effective of the atypical antipsychotics; however, negative symptoms may persist, indicating a critical need to develop augmenting treatment approaches. METHODS: A retrospective chart review evaluated outcomes for 5 young adult inpatients with treatment-resistant schizophrenia who were prescribed off-label oxytocin (OT; 10 IU/sublingual, 1 time per day, to 20 IU/sublingual, 3 times per day) after their therapeutic response to clozapine plateaued (dose range: 200 to 600 mg). The augmented treatment was well tolerated and continued for at least 1 year after discharge from the hospital, with continued outpatient follow-up by the treating psychiatrist. Evaluation included the Positive and Negative Syndrome Scale and clinical review based on both self and parent/guardian reports. RESULTS: The augmentation of clozapine with sublingual OT in young adults with treatment-resistant schizophrenia appeared to reduce negative symptoms, maintain lowered positive symptoms, and increase occupational and social functioning (eg, return to work or school), as noted by family members. CONCLUSIONS: Future controlled, prospective studies should investigate the possibility that OT can significantly reduce negative symptoms of chronic psychotic illnesses that are inadequately responsive to clozapine or other antipsychotic medications alone.

Accurate Thermochemistry for Organic Cations via Error Cancellation using Connectivity-Based Hierarchy.

Connectivity-Based Hierarchy (CBH) is an effective error-cancellation scheme for the determination of chemically accurate thermochemical properties of a variety of organic and biomolecules. Neutral molecules and open-shell radicals have already been treated successfully by this approach utilizing inexpensive computational methods such as density functional theory. Herein, we present an extension of the method to a new class of molecules, specifically, organic cations. Because of the presence of structural rearrangements involving hydrogen migrations as well as unusual structures such as bridged cations, the application of the standard CBH protocol to a test set of 25 cations leads to significant errors due to ineffective bond-type matching. We propose an adjusted protocol to overcome such limitations to achieve highly effective error cancellation. The modified CBH methods, in conjunction with a wide range of density functionals, reproduce G4 energies for the test set of organic cations accurately within 1-2 kcal/mol at a reduced computational cost.

Stepping-Stone CBH: Benchmark and Application of a Multilayered Isodesmic-Based Correction Scheme.

We present a generalization of the connectivity-based hierarchy (CBH) of isodesmic-based correction schemes to a multilayered fragmentation platform for overall cost reduction while retaining high accuracy. The newly developed multilayered CBH approach, called stepping-stone CBH (SSCBH), is benchmarked on a diverse set of 959 medium-sized organic molecules. Applying SSCBH corrections to the PBEh-D3 density functional resulted in an average error of 0.76 kcal/mol for the full test set compared to accurate CCSD(T)-quality enthalpies and an even lower error of 0.44 kcal/mol on a subset containing only acyclic molecules. These results rival the traditional CBH-3 approach at a greatly reduced cost, allowing larger fragment corrections to be made at the MP2 level of theory rather than with G4. Our SSCBH approach will enable more widespread applications of CBH methods to a broader range of organic and biomolecular systems.

Comparative Efficacy of Advanced Therapies in the Treatment of Radiographic Axial Spondyloarthritis or Ankylosing Spondylitis as Evaluated by the ASDAS Low Disease Activity Criteria.

INTRODUCTION: With an increasing number of biologic/targeted synthetic disease-modifying antirheumatic drug options available for the treatment of active ankylosing spondylitis (AS), also known as radiographic axial spondyloarthritis, it is of clinical interest to determine the comparative efficacy of these advanced therapies among populations with differing prior advanced therapy exposure. This study aimed to assess the comparative efficacy of approved advanced therapies for AS in tumor necrosis factor inhibitor (TNFi)-naïve and, separately, in TNFi inadequate responder/intolerant (-IR) populations. METHODS: A systematic literature review was conducted to identify randomized clinical trials for TNFis, interleukin-17A inhibitors, and Janus kinase inhibitors used as advanced therapies for active AS. Clinical efficacy was considered by the Ankylosing Spondylitis Disease Activity Score low disease activity (ASDAS LDA) criteria, defined as ASDAS score less than 2.1, among approved therapies. Comparative efficacy in the TNFi-naïve population was assessed utilizing network meta-analysis, while comparative efficacy in the TNFi-IR population was assessed utilizing matching-adjusted indirect comparison. Odds ratios were calculated, from which absolute rates and numbers needed to treat were calculated. Safety in the form of trial-reported and placebo-adjusted rates of discontinuation due to adverse events (AEs) was reviewed. RESULTS: Among the TNFi-naïve population, the estimated ASDAS LDA rate between week 12 and 16 was highest for patients treated with upadacitinib (52.8%) and lowest for patients treated with placebo (11.6%). Among the TNFi-IR population, the estimated ASDAS LDA rate was 41.3% for patients treated with upadacitinib and 17.5% for patients treated with ixekizumab. The trial-reported and placebo-adjusted rates of discontinuation due to AEs were generally low across included advanced therapies. CONCLUSIONS: Relative to other assessed therapies, upadacitinib demonstrated greater clinical efficacy per ASDAS LDA in the treatment of active AS in both TNFi-naïve and TNFi-IR populations. Head-to-head and real-world data comparisons are warranted to both validate these findings and aid medical decision makers.

Interpretable Graph-Network-Based Machine Learning Models via Molecular Fragmentation.

Chemists have long benefitted from the ability to understand and interpret the predictions of computational models. With the current shift to more complex deep learning models, in many situations that utility is lost. In this work, we expand on our previously work on computational thermochemistry and propose an interpretable graph network, FragGraph(nodes), that provides decomposed predictions into fragment-wise contributions. We demonstrate the usefulness of our model in predicting a correction to density functional theory (DFT)-calculated atomization energies using Δ-learning. Our model predicts G4(MP2)-quality thermochemistry with an accuracy of <1 kJ mol-1 for the GDB9 dataset. Besides the high accuracy of our predictions, we observe trends in the fragment corrections which quantitatively describe the deficiencies of B3LYP. Node-wise predictions significantly outperform our previous model predictions from a global state vector. This effect is most pronounced as we explore the generality by predicting on more diverse test sets indicating node-wise predictions are less sensitive to extending machine learning models to larger molecules.

Clinical and Economic Benefit of Advanced Therapies for the Treatment of Active Ankylosing Spondylitis.

INTRODUCTION: Recent changes to treatment guidelines for ankylosing spondylitis (AS) have listed first-line advanced therapies as tumor necrosis factor (TNF), interleukin (IL)-17, and Janus kinase (JAK) inhibitors. This study sought to assess the comparative clinical and economic benefit of advanced therapies approved for AS. METHODS: A systematic literature review was conducted to identify randomized clinical trials for JAK inhibitors (upadacitinib [UPA], tofacitinib [TOF]), anti-IL-17 therapies (secukinumab [SEC], ixekizumab [IXE]), and TNF inhibitors (adalimumab [ADA], etanercept [ETN], golimumab [GOL]) used for the treatment of active AS. Clinical efficacy was evaluated by Assessment of Spondyloarthritis International Society 40 (ASAS40) criteria and treatment discontinuation due to adverse events (AEs) was used to generate response rates synthesized via a Bayesian network meta-analysis. Number needed to treat (NNT) was calculated as the reciprocal of incremental response rate of each treatment versus placebo. Cost per ASAS40 responder (CPR) was calculated as the 12-week treatment costs divided by ASAS40 response rates. Data were stratified by biologic treatment status (i.e., biologic naïve [bio-naïve] or inadequate response or intolerance to biologics [bio-IR]) for efficacy and CPR analyses. RESULTS: Among bio-naïve patients, the response rate for ASAS40 was 53.6% for UPA-treated patients, whereas most other treatments had response rates between 41% and 49%. NNTs were lowest for UPA-treated patients at 2.8 (other therapies 3.2-4.8). Estimated CPR among UPA-treated patients was lowest (UPA $39.5k vs others $44.2k-102.5k). Efficacy and CPR trends were similar among bio-IR and TNF-IR patients. Among bio-naïve and bio-IR patients, the rate of AEs leading to discontinuation was lowest among UPA and SEC-treated patients (0.0, others 0.6-3.7%). CONCLUSIONS: Relative to other treatments assessed in this study, UPA demonstrated numerically greater clinical and economic benefit for the treatment of AS. Head-to-head or real-world comparisons of these therapies are warranted and may inform clinical decision-making.

Approaching Coupled Cluster Accuracy with Density Functional Theory Using the Generalized Connectivity-Based Hierarchy.

This Perspective reviews connectivity-based hierarchy (CBH), a systematic hierarchy of error-cancellation schemes developed in our group with the goal of achieving chemical accuracy using inexpensive computational techniques ("coupled cluster accuracy with DFT"). The hierarchy is a generalization of Pople's isodesmic bond separation scheme that is based only on the structure and connectivity and is applicable to any organic and biomolecule consisting of covalent bonds. It is formulated as a series of rungs involving increasing levels of error cancellation on progressively larger fragments of the parent molecule. The method and our implementation are discussed briefly. Examples are given for the applications of CBH involving (1) energies of complex organic rearrangement reactions, (2) bond energies of biofuel molecules, (3) redox potentials in solution, (4) pKa predictions in the aqueous medium, and (5) theoretical thermochemistry combining CBH with machine learning. They clearly show that near-chemical accuracy (1-2 kcal/mol) is achieved for a variety of applications with DFT methods irrespective of the underlying density functional used. They demonstrate conclusively that seemingly disparate results, often seen with different density functionals in many chemical applications, are due to an accumulation of systematic errors in the smaller local molecular fragments that can be easily corrected with higher-level calculations on those small units. This enables the method to achieve the accuracy of the high level of theory (e.g., coupled cluster) while the cost remains that of DFT. The advantages and limitations of the method are discussed along with areas of ongoing developments.

Comparative Efficacy of Targeted Systemic Therapies for Moderate-to-Severe Atopic Dermatitis without Topical Corticosteroids: An Updated Network Meta-analysis.

INTRODUCTION: The treatment landscape for moderate-to-severe atopic dermatitis (AD) continues to expand. This network meta-analysis (NMA) updates a previously conducted NMA to include data from the most recent phase 3 trials to assess the comparative efficacy of targeted systemic therapies without the addition of topical corticosteroids (TCS) and/or topical calcineurin inhibitors (TCI) in adults with moderate-to-severe AD. METHODS: Data from recent phase 3 monotherapy trials of lebrikizumab, ADvocate1 (NCT04146363) and ADvocate2 (NCT04178967), were included in the analyses, along with other eligible phase 3/4 randomized placebo-controlled trials for abrocitinib, baricitinib, dupilumab, tralokinumab, and upadacitinib identified through a systemic literature review in Silverberg et al. (Dermatol Ther (Heidelb) 12(5):1181-1196, 2022). The proportion of patients achieving Eczema Area and Severity Index (EASI) improvement ≥ 90% from baseline (EASI-90), EASI improvement ≥ 75% from baseline (EASI-75), ≥ 4-point improvement on Pruritus Numerical Rating Scale from baseline (ΔNRS ≥ 4), and Investigator Global Assessment (IGA) score of 0 or 1 (clear or almost clear) and reduction of ≥ 2 points from baseline (IGA 0/1) were evaluated using a Bayesian network meta-analysis. RESULTS: The updated NMA analyzed 13 unique placebo-controlled trials involving 7105 patients in 32 arms across 6 targeted therapies. Upadacitinib 30 mg was the most efficacious therapy across all endpoints at the primary timepoint (week 12 or 16) and at earlier timepoints, generally followed by abrocitinib 200 mg, upadacitinib 15 mg, dupilumab 300 mg, and lebrikizumab 250 mg or abrocitinib 100 mg. Baricitinib 2 mg and tralokinumab were generally ranked lower across outcomes. CONCLUSIONS: Many factors need to be considered for treatment selection for AD, especially as new treatments continue to emerge. After incorporating recent placebo-controlled phase 3 data of lebrikizumab, upadacitinib 30 mg, upadacitinib 15 mg, and abrocitinib 200 mg remain the most efficacious targeted systemic therapies over 12-16 weeks of therapy in AD. These updated findings can help healthcare providers when creating a patient's personalized treatment plan.

Efficacy and Safety of Advanced Therapies for Moderately to Severely Active Ulcerative Colitis at Induction and Maintenance: An Indirect Treatment Comparison Using Bayesian Network Meta-analysis.

Background: Given rapid innovation in advanced therapies for moderately to severely active ulcerative colitis (UC), we investigated their comparative efficacy and safety during induction and maintenance through network meta-analysis. Methods: Using Bayesian methods, endpoints of clinical remission and clinical response per Full Mayo score, and endoscopic improvement were assessed in bio-naive and -exposed populations. Safety was assessed in overall populations by all adverse events (AEs), serious AEs, discontinuation due to AEs, and serious infections. Phase 3 randomized controlled trials were identified via systematic literature review, including the following advanced therapies: infliximab, adalimumab, vedolizumab, golimumab, tofacitinib, ustekinumab, filgotinib, ozanimod, and upadacitinib. Random effects models were used to address between-study heterogeneity. Intent-to-treat (ITT) efficacy rates were calculated by adjusting maintenance outcomes by likelihood of induction response. Results: Out of 48 trials identified, 23 were included. Across all outcomes and regardless of prior biologic exposure, ITT efficacy rates were highest for upadacitinib, owing to its highest ranking for all efficacy outcomes in induction and for all but clinical remission during maintenance among bio-naive induction responders. For all advanced therapies versus placebo, there were no significant differences in serious AEs or serious infections across therapies. For all AEs, golimumab had higher odds versus placebo during maintenance; for discontinuation due to AEs, upadacitinib had lower odds versus placebo during induction, while ustekinumab and vedolizumab had lower odds versus placebo during maintenance. Conclusions: Upadacitinib may be the most efficacious therapy for moderately to severely active UC based on ITT analyses, with similar safety across advanced therapies.

Prevalence and Overlap of Cardiac, Renal, and Metabolic Conditions in US Adults, 1999-2020.

Importance: Individually, cardiac, renal, and metabolic (CRM) conditions are common and leading causes of death, disability, and health care-associated costs. However, the frequency with which CRM conditions coexist has not been comprehensively characterized to date. Objective: To examine the prevalence and overlap of CRM conditions among US adults currently and over time. Design, Setting, and Participants: To establish prevalence of CRM conditions, nationally representative, serial cross-sectional data included in the January 2015 through March 2020 National Health and Nutrition Examination Survey (NHANES) were evaluated in this cohort study. To assess temporal trends in CRM overlap, NHANES data between 1999-2002 and 2015-2020 were compared. Data on 11 607 nonpregnant US adults (≥20 years) were included. Data analysis occurred between November 10, 2020, and November 23, 2022. Main Outcomes and Measures: Proportion of participants with CRM conditions, overall and stratified by age, defined as cardiovascular disease (CVD), chronic kidney disease (CKD), type 2 diabetes (T2D), or all 3. Results: From 2015 through March 2020, of 11 607 US adults included in the analysis (mean [SE] age, 48.5 [0.4] years; 51.0% women), 26.3% had at least 1 CRM condition, 8.0% had at least 2 CRM conditions, and 1.5% had 3 CRM conditions. Overall, CKD plus T2D was the most common CRM dyad (3.2%), followed by CVD plus T2D (1.7%) and CVD plus CKD (1.6%). Participants with higher CRM comorbidity burden were more likely to be older and male. Among participants aged 65 years or older, 33.6% had 1 CRM condition, 17.1% had 2 CRM conditions, and 5.0% had 3 CRM conditions. Within this subset, CKD plus T2D (7.3%) was most common, followed by CVD plus CKD (6.0%) and CVD plus T2D (3.8%). The CRM comorbidity burden was disproportionately high among participants reporting non-Hispanic Black race or ethnicity, unemployment, low socioeconomic status, and no high school degree. Among participants with 3 CRM conditions, nearly one-third (30.5%) did not report statin use, and only 4.8% and 3.0% used glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter 2 inhibitors, respectively. Between 1999 and 2020, the proportion of US adults with multiple CRM conditions increased significantly (from 5.3% to 8.0%; P < .001 for trend), as did the proportion having all 3 CRM conditions (0.7% to 1.5%; P < .001 for trend). Conclusions and Relevance: This cohort study found that CRM multimorbidity is increasingly common and undertreated among US adults, highlighting the importance of collaborative and comprehensive management strategies.

Comparative Efficacy of Targeted Systemic Therapies for Moderate to Severe Atopic Dermatitis without Topical Corticosteroids: Systematic Review and Network Meta-analysis.

INTRODUCTION: The comparative efficacy of targeted systemic therapies for moderate to severe atopic dermatitis (AD) has not been systematically assessed using recent phase 3 data. This network meta-analysis assesses the comparative efficacy of targeted systemic therapies without the addition of topical corticosteroids (TCS) and/or topical calcineurin inhibitors (TCI) in adults with moderate to severe AD. METHODS: The systematic literature review searched through 17 May 2021 for phase 3/4 trials with upadacitinib, interleukin-4 (IL-4), interleukin-13 (IL-13), or JAK inhibitors compared with placebo or active intervention for adults and adolescents with moderate to severe AD with inadequate response to TCS/TCI or for whom TCS/TCI was medically inadvisable, without restrictions on year or region. Researchers assessed data using PRISMA guidelines. The proportion of patients achieving trial co-primary endpoints [Investigator Global Assessment (IGA) score of 0 or 1 (clear or almost clear) and reduction of ≥ 2 points from baseline; proportion of patients achieving Eczema Area and Severity Index (EASI) improvement ≥ 75% from baseline (EASI-75)]; EASI improvement ≥ 90% from baseline (EASI-90); and ≥ 4-point improvement on Pruritus Numerical Rating Scale from baseline (ΔNRS ≥ 4) were evaluated using Bayesian network meta-analysis. RESULTS: Of 3415 initially identified records, network meta-analysis (NMA) ultimately included 6 records representing 9 unique studies. Two upadacitinib trials were also included. Eleven clinical trials including 6254 patients were analyzed. Upadacitinib 30 mg daily was the most efficacious therapy across all endpoints at the primary endpoint (week 12 or 16) and at earlier timepoints, followed by upadacitinib 15 mg daily and abrocitinib 200 mg daily. DISCUSSION: Many factors need to be considered for treatment selection for AD. These findings can help healthcare providers when personalizing a patient's treatment. CONCLUSION: Upadacitinib 30 mg daily, upadacitinib 15 mg daily, and abrocitinib 200 mg daily may be the most efficacious targeted systemic therapies over 12-16 weeks of therapy in AD.

Changing Medical Education When Change Is Hard: Implementing an Interdepartmental Entrustable Professional Activity.

BACKGROUND: Entrustable professional activities (EPAs) are a new tool for assessing learners that represents a significant movement in graduate medical education (GME) toward competency-based assessment and serves as a bridge between milestones and clinical practice. Whenever a major change is implemented to any system, resistance to change is expected. Many change management models have been proposed to overcome this resistance; a newer model is outlined in the book Switch. The objective was to describe the change management principles used to institute an EPA. METHODS: The model introduced in Switch was used as a framework for implementing a trauma resuscitation EPA in a joint effort between the departments of surgery and emergency medicine (EM) at the University of Wisconsin Hospitals and Clinics. The Department of Emergency Medicine used the principles of change management, completing 295 faculty evaluations of trauma resuscitations compared to the Department of Surgery, whose faculty completed 50 evaluations between the study period of July 2018 through October 2019. A survey completed winter 2019 of EM faculty was used to determine the most important principles toward successful implementation. RESULTS: Twenty-seven of 35 (78% response rate) of EM faculty identified key concepts from Switch as being instrumental in the successful implementation. Internal discussion of the implementation approach used by the Department of Surgery identified several limitations that would have been overcome by using these same change management principles. CONCLUSION: We conclude that the principles of change management provide a useful framework for successfully implementing EPAs into GME.

Effective Molecular Descriptors for Chemical Accuracy at DFT Cost: Fragmentation, Error-Cancellation, and Machine Learning.

Recent advances in theoretical thermochemistry have allowed the study of small organic and bio-organic molecules with high accuracy. However, applications to larger molecules are still impeded by the steep scaling problem of highly accurate quantum mechanical (QM) methods, forcing the use of approximate, more cost-effective methods at a greatly reduced accuracy. One of the most successful strategies to mitigate this error is the use of systematic error-cancellation schemes, in which highly accurate QM calculations can be performed on small portions of the molecule to construct corrections to an approximate method. Herein, we build on ideas from fragmentation and error-cancellation to introduce a new family of molecular descriptors for machine learning modeled after the Connectivity-Based Hierarchy (CBH) of generalized isodesmic reaction schemes. The best performing descriptor ML(CBH-2) is constructed from fragments preserving only the immediate connectivity of all heavy (non-H) atoms of a molecule along with overlapping regions of fragments in accordance with the inclusion-exclusion principle. Our proposed approach offers a simple, chemically intuitive grouping of atoms, tuned with an optimal amount of error-cancellation, and outperforms previous structure-based descriptors using a much smaller input vector length. For a wide variety of density functionals, DFT+ΔML(CBH-2) models, trained on a set of small- to medium-sized organic HCNOSCl-containing molecules, achieved an out-of-sample MAE within 0.5 kcal/mol and 2σ (95%) confidence interval of <1.5 kcal/mol compared to accurate G4 reference values at DFT cost.

Implementation of Entrustable Professional Activities into a General Surgery Residency.

OBJECTIVE: Concerns over resident ability to practice effectively after graduation have led to the competency-based medical education movement. Entrustable professional activities (EPAs) may facilitate competency-based medical education in surgery, but implementation is challenging. This manuscript describes 1 strategy used to implement EPAs into an academic general surgery residency. DESIGN, SETTING, PARTICIPANTS: A mobile application was developed incorporating 5 EPAs developed by the American Board of Surgery; residents and faculty from the Departments of Surgery, Emergency Medicine, and Hospital Medicine at a single tertiary care center were trained in its use. Entrustment levels and free text feedback were collected. Self-assessment was paired with supervisor assessment, and faculty assessments were used to inform clinical competency committee entrustment decisions. Feedback was regularly solicited from app users and results distributed on a monthly basis. RESULTS: One thousand seven hundred and twenty microassessments were collected over the first 16 months of implementation; 898 (47.8%) were performed by faculty with 569 (66.0%) matched pairs. Engagement was skewed with small numbers of high performers in both resident and faculty groups. Continued development of resident and faculty was required to sustain engagement with the program. Nonsurgical specialties contributed significantly to resident assessments (496, 28.8%). CONCLUSIONS: EPAs are being successfully integrated into the assessment framework at our institution. EPA implementation in surgery residency is a long-term process that requires investment, but may address limitations in the current assessment framework.

Insane: James Holmes, Clark v. Arizona, and America's Insanity Defense.

Insanity is a legal term of art that changes definitions depending on the legal standard in American jurisprudence, which explains why a man who mental health professionals described as having an uncontrollable obsession with killing people can be found not insane and guilty. This Note addresses the current state of the Insanity Defense Reform Act of 1984 and its widespread implementation at the state level. Part II supplies background information on the history of the insanity defense and how it has transformed over the years in American jurisprudence. Part III provides an analysis of the of the insanity defense. Part IV suggests a new standard of for the insanity defense with a more accommodating application to a wider degree of mental diseases.

Smoked cocaine self-administration by humans is not reduced by large gabapentin maintenance doses.

Previously, we reported that gabapentin, a lambda-aminobutyric acid (GABA) agonist, significantly reduced "positive" subjective effects of cocaine without reducing cocaine self-administration. We speculated that the gabapentin doses used in that study were too low to detect subtle shifts in the reinforcing effects of cocaine. Thus, the present study examined the effects of larger gabapentin maintenance doses on cocaine-related effects, including self-administration. During this 48-day double-blind, crossover design study, the effects of gabapentin maintenance (0, 2400, 3200 mg/day) on response to cocaine (0, 12, 25, 50 mg) were investigated in six cocaine-dependent individuals not seeking treatment for their cocaine use. Active cocaine significantly increased choice to self-administer cocaine, subjective-effect ratings (e.g., "Good Drug Effect"), blood pressure, and heart rate. Gabapentin did not decrease cocaine self-administration, cardiovascular measures, or most subjective effects of cocaine. These data agree with findings from a clinical trial examining the effects of similar gabapentin doses on cocaine use by treatment-seeking cocaine-dependent individuals and suggest that gabapentin does not show promise as a treatment medication for cocaine dependence.

Using buprenorphine to facilitate entry into residential therapeutic community rehabilitation.

For opioid-dependent patients, the need for detoxification has been a barrier to entry into long-term residential treatment. This report describes a retrospective observational cohort study with the first 38 opioid-dependent patients entering First Step, a 14-day buprenorphine-naloxone (Suboxone) detoxification regimen integrated into a long-term residential therapeutic community (TC) program. Eighty-nine percent (34 of 38) of First Step patients completed a 14-day buprenorphine taper protocol, 50% (19 of 38) completed an initial 3- to 4-week stay, and 39% (15 of 38) completed at least 3 months of residential treatment at the TC. Retention did not differ significantly in a demographically matched concurrently admitted control group without impending opioid withdrawal, in which 65% (24 of 37) completed an initial 3- to 4-week stay (p = .20) and 57% (21 of 37) completed at least 3 months of treatment (p = .14). Withdrawal symptoms were mild, and there were no instances of precipitated withdrawal. The findings suggest the potential for buprenorphine to serve as a bridge, improving the viability of long-term residential treatment for managing opioid dependence.