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Impact/Impasse argues for the value of everyday life in college classrooms. Quantifiable categories such as high-impact practice, student engagement, and integrative learning have captured the imagination of a generation of higher education researchers, practitioners, administrators, and policymakers. But they miss those mundane moments, or \"impasses,\" that resist capture by metrics while nevertheless shaping student outcomes. Impact/Impasse blends critical theories and ethnographic research-conducted before and during the COVID-19 pandemic-to argue that learning happens in ordinary moments. Indeed, in sharing anecdotes from both in-person and virtual classrooms, the coauthors show how the so-called new normal is little different from the old in its neoliberal attachment to data. Impact/Impasse provides a conceptual and practical foundation for an alternative approach to valuing impacts on their own terms, in excess of quantification.

Vaccine effectiveness studies have not differentiated the effect of the delta (B.1.617.2) variant and potential waning immunity in observed reductions in effectiveness against SARS-CoV-2 infections. We aimed to evaluate overall and variant-specific effectiveness of BNT162b2 (tozinameran, Pfizer–BioNTech) against SARS-CoV-2 infections and COVID-19-related hospital admissions by time since vaccination among members of a large US health-care system. In this retrospective cohort study, we analysed electronic health records of individuals (≥12 years) who were members of the health-care organisation Kaiser Permanente Southern California (CA, USA), to assess BNT162b2 vaccine effectiveness against SARS-CoV-2 infections and COVID-19-related hospital admissions for up to 6 months. Participants were required to have 1 year or more previous membership of the organisation. Outcomes comprised SARS-CoV-2 PCR-positive tests and COVID-19-related hospital admissions. Effectiveness calculations were based on hazard ratios from adjusted Cox models. This study was registered with ClinicalTrials.gov, NCT04848584. Between Dec 14, 2020, and Aug 8, 2021, of 4 920 549 individuals assessed for eligibility, we included 3 436 957 (median age 45 years [IQR 29–61]; 1 799 395 [52·4%] female and 1 637 394 [47·6%] male). For fully vaccinated individuals, effectiveness against SARS-CoV-2 infections was 73% (95% CI 72–74) and against COVID-19-related hospital admissions was 90% (89–92). Effectiveness against infections declined from 88% (95% CI 86–89) during the first month after full vaccination to 47% (43–51) after 5 months. Among sequenced infections, vaccine effectiveness against infections of the delta variant was high during the first month after full vaccination (93% [95% CI 85–97]) but declined to 53% [39–65] after 4 months. Effectiveness against other (non-delta) variants the first month after full vaccination was also high at 97% (95% CI 95–99), but waned to 67% (45–80) at 4–5 months. Vaccine effectiveness against hospital admissions for infections with the delta variant for all ages was high overall (93% [95% CI 84–96]) up to 6 months. Our results provide support for high effectiveness of BNT162b2 against hospital admissions up until around 6 months after being fully vaccinated, even in the face of widespread dissemination of the delta variant. Reduction in vaccine effectiveness against SARS-CoV-2 infections over time is probably primarily due to waning immunity with time rather than the delta variant escaping vaccine protection. Pfizer.

Pregnancy increases the risk of periodontitis due to the increase in progesterone and estrogen. Moreover, periodontitis during pregnancy is associated with development of pregnancy and birth related complications. The aim of this study is to determine, whether periodontal treatment during pregnancy can reduce systemic inflammation and lower the risk of adverse pregnancy and birth related outcomes. The PROBE study is a non-randomized controlled intervention study conducted among 600 pregnant women with periodontitis. The women will be recruited among all pregnant women at two Danish hospitals in Region Zealand during their nuchal translucency scan and will subsequently be screened for periodontitis. The intervention group includes 300 pregnant women, who will be offered state-of-the-art periodontal treatment during pregnancy. The control group includes additional 300 pregnant women, who will be offered periodontal treatment after giving birth. Outcome measures include periodontal measures, inflammatory, hormonal and glycaemic markers as well as the prevalence of preterm birth risk, low birth weight and risk markers of gestational diabetes mellitus (GDM) and preeclampsia that will be collected from all screened women and further during pregnancy week 20 and pregnancy week 35 for women enrolled in the intervention. The study's findings will be published in peer reviewed journals and disseminated at national and international conferences and through social media. The PROBE study is designed to provide important new knowledge as to whether periodontal treatment during pregnancy can reduce the prevalence of complications related to pregnancy and birth. The study was registered on clinicaltrials.gov (NCT06110143).

California data demonstrate failures in latent tuberculosis screening to prevent progression to tuberculosis disease. Therefore, we developed a clinical risk prediction model for tuberculosis disease using electronic health records. This study included Kaiser Permanente Southern California and Northern California members ≥18 years during 2008-2019. Models used Cox proportional hazards regression, Harrell’s C-statistic, and a simulated TB disease outcome accounting for cases prevented by current screening which includes both observed and simulated cases. We compared sensitivity and number-needed-to-screen for model-identified high-risk individuals with current screening. Of 4,032,619 and 4,051,873 Southern and Northern California members, tuberculosis disease incidences were 4.1 and 3.3 cases per 100,000 person-years, respectively. The final model C-statistic was 0.816 (95% simulation interval 0.805-0.824). Model sensitivity screening high-risk individuals was 0.70 (0.68-0.71) and number-needed-to-screen was 662 (646-679) persons-per tuberculosis disease case, compared to a sensitivity of 0.36 (0.34-0.38) and number-needed-to-screen of 1632 (1485-1774) with current screening. Here, we show our predictive model improves tuberculosis screening efficiency in California. In the United States, tuberculosis control is focused on prevention of progression from latent tuberculosis infection to TB disease. Here, the authors develop and validate a prediction model to identify individuals at risk of TB disease using data from electronic health records from California.

Uterine leiomyomas and adenomyosis are both common and often associated with abnormal uterine bleeding (AUB), including the symptom of heavy menstrual bleeding (HMB). Understanding the prevalence of adenomyosis in women with uterine leiomyomas could inform clinicians and patients in a way that may improve therapeutic approaches. To explore the prevalence of adenomyosis in a group of women who underwent hysterectomy for AUB-L, to determine the prevalence of submucous leiomyomas, and to examine the utility of preoperative ultrasound to detect the presence of adenomyosis. The Kaiser Permanente Hysterectomy Database (KPHD) was searched for women aged 18-52 undergoing hysterectomy for leiomyoma-associated chronic AUB (AUB-L) in 2018 and 2019. A target sample of 400 comprised those with at least 3 years in the Health System. Radiologists evaluated preoperative pelvic ultrasound images to determine leiomyoma size and level 2 FIGO type (submucous or other), and the linked electronic medical record abstracted for clinical features, including histopathological evidence of adenomyosis. Of the 370 subjects that met the study criteria, adenomyosis was identified via histopathology in 170 (45.9%). There was no difference in the adenomyosis prevalence with (47.1%) and without (43.0%) at least one submucous leiomyoma. Subgroup analysis of ultrasound images by an expert radiologist for the presence of adenomyosis demonstrated a positive predictive value of 54.0% and a negative predictive value of 43.4%. Adenomyosis was present in almost half of this AUB-L cohort undergoing hysterectomy and was equally prevalent in those with and without submucous leiomyomas as determined by sonographic evaluation. The imaging findings are in accord with prior investigators and demonstrate that 2-D ultrasound is insensitive to the presence of adenomyosis when the uterus is affected by leiomyomas. Further research is necessary to determine the impact of various adenomyosis phenotypes on the presence and severity of the symptom of HMB.

Though targeted testing for latent tuberculosis infection (\"LTBI\") for persons born in countries with high tuberculosis incidence (\"HTBIC\") is recommended in health care settings, this information is not routinely recorded in the electronic health record (\"EHR\"). We develop and validate a prediction model for birth in a HTBIC using EHR data. In a cohort of patients within Kaiser Permanente Southern California (\"KPSC\") and Kaiser Permanent Northern California (\"KPNC\") between January 1, 2008 and December 31, 2019, KPSC was used as the development dataset and KPNC was used for external validation using logistic regression. Model performance was evaluated using area under the receiver operator curve (\"AUCROC\") and area under the precision and recall curve (\"AUPRC\"). We explored various cut-points to improve screening for LTBI. KPSC had 73% and KPNC had 54% of patients missing country-of-birth information in the EHR, leaving 2,036,400 and 2,880,570 patients with EHR-documented country-of-birth at KPSC and KPNC, respectively. The final model had an AUCROC of 0.85 and 0.87 on internal and external validation datasets, respectively. It had an AUPRC of 0.69 and 0.64 (compared to a baseline HTBIC-birth prevalence of 0.24 at KPSC and 0.19 at KPNC) on internal and external validation datasets, respectively. The cut-points explored resulted in a number needed to screen from 7.1-8.5 persons/positive LTBI diagnosis, compared to 4.2 and 16.8 persons/positive LTBI diagnosis from EHR-documented birth in a HTBIC and current screening criteria, respectively. Using logistic regression with EHR data, we developed a simple yet useful model to predict birth in a HTBIC which decreased the number needed to screen compared to current LTBI screening criteria. Our model improves the ability to screen for LTBI in health care settings based on birth in a HTBIC.

Background Dexterous manipulation of the hand, one of the features of human motor control, is often compromised after stroke, to the detriment of basic functions. Despite the importance of independent movement of the digits to activities of daily living, relatively few studies have assessed the impact of specifically targeting individuated movements of the digits on hand rehabilitation. The purpose of this study was to investigate the impact of such finger individuation training, by means of a novel mechatronic-virtual reality system, on fine motor control after stroke. Methods An actuated virtual keypad (AVK) system was developed in which the impaired hand controls a virtual hand playing a set of keys. Creation of individuated digit movements is assisted by a pneumatically actuated glove, the PneuGlove. A study examining efficacy of the AVK system was subsequently performed. Participants had chronic, moderate hand impairment resulting from a single stroke incurred at least 6 months prior. Each subject underwent 18 hour-long sessions of extensive therapy (3x per week for 6 weeks) targeted at finger individuation. Subjects were randomly divided into two groups: the first group (Keypad: N = 7) utilized the AVK system while the other group (OT: N = 7) received a similarly intensive dose of occupational therapy; both groups worked directly with a licensed occupational therapist. Outcome measures such as the Jebsen-Taylor Hand Function Test (JTHFT), Action research Arm Test (ARAT), Fugl-Meyer Upper Extremity Motor Assessment/Hand subcomponent (FMUE/FMH), grip and pinch strengths were collected at baseline, post-treatment and one-month post-treatment. Results While both groups exhibited some signs of change after the training sessions, only the Keypad group displayed statistically significant improvement both for measures of impairment (FMH: p = 0.048) and measures of task performance (JTHFT: p = 0.021). Additionally, the finger individuation index – a measure of finger independence – improved only for the Keypad group after training (p = 0.05) in the subset (Keypad: N = 4; OT: N = 5) of these participants for which it was measured. Conclusions Actively assisted individuation therapy comprised of non task-specific modalities, such as can be achieved with virtual platforms like the AVK described here, may prove to be valuable clinical tools for increasing the effectiveness and efficiency of therapy following stroke.

Natural language processing (NLP) of unstructured text from electronic medical records (EMR) can improve the characterization of COVID-19 signs and symptoms, but large-scale studies demonstrating the real-world application and validation of NLP for this purpose are limited. The aim of this paper is to assess the contribution of NLP when identifying COVID-19 signs and symptoms from EMR. This study was conducted in Kaiser Permanente Southern California, a large integrated health care system using data from all patients with positive SARS-CoV-2 laboratory tests from March 2020 to May 2021. An NLP algorithm was developed to extract free text from EMR on 12 established signs and symptoms of COVID-19, including fever, cough, headache, fatigue, dyspnea, chills, sore throat, myalgia, anosmia, diarrhea, vomiting or nausea, and abdominal pain. The proportion of patients reporting each symptom and the corresponding onset dates were described before and after supplementing structured EMR data with NLP-extracted signs and symptoms. A random sample of 100 chart-reviewed and adjudicated SARS-CoV-2-positive cases were used to validate the algorithm performance. A total of 359,938 patients (mean age 40.4 [SD 19.2] years; 191,630/359,938, 53% female) with confirmed SARS-CoV-2 infection were identified over the study period. The most common signs and symptoms identified through NLP-supplemented analyses were cough (220,631/359,938, 61%), fever (185,618/359,938, 52%), myalgia (153,042/359,938, 43%), and headache (144,705/359,938, 40%). The NLP algorithm identified an additional 55,568 (15%) symptomatic cases that were previously defined as asymptomatic using structured data alone. The proportion of additional cases with each selected symptom identified in NLP-supplemented analysis varied across the selected symptoms, from 29% (63,742/220,631) of all records for cough to 64% (38,884/60,865) of all records with nausea or vomiting. Of the 295,305 symptomatic patients, the median time from symptom onset to testing was 3 days using structured data alone, whereas the NLP algorithm identified signs or symptoms approximately 1 day earlier. When validated against chart-reviewed cases, the NLP algorithm successfully identified signs and symptoms with consistently high sensitivity (ranging from 87% to 100%) and specificity (94% to 100%). These findings demonstrate that NLP can identify and characterize a broad set of COVID-19 signs and symptoms from unstructured EMR data with enhanced detail and timeliness compared with structured data alone.

ObjectivesAssess the association between tocilizumab administration and clinical outcomes among mechanically ventilated patients with COVID-19 pneumonia.DesignRetrospective cohort study.SettingLarge integrated health system with 9 million members in California, USA.Participants4185 Kaiser Permanente members hospitalised with COVID-19 pneumonia requiring invasive mechanical ventilation (IMV).InterventionsReceipt of tocilizumab within 10 days of initiation of IMV.Outcome measuresUsing a retrospective cohort of consecutive patients hospitalised with COVID-19 pneumonia who required IMV in a large integrated health system in California, USA, we assessed the association between tocilizumab administration and 28-day mortality, time to extubation from IMV and time to hospital discharge.ResultsAmong 4185 patients, 184 received tocilizumab and 4001 patients did not receive tocilizumab within 10 days of initiation of IMV. After inverse probability weighting, baseline characteristics were well balanced between groups. Patients treated with tocilizumab had a similar risk of death in the 28 days after intubation compared with patients not treated with tocilizumab (adjusted HR (aHR), 1.21, 95% CI 0.98 to 1.50), but did have a significantly longer time-to-extubation (aHR 0.71; 95% CI 0.57 to 0.88) and time-to-hospital-discharge (aHR 0.66; 95% CI 0.50 to 0.88). However, patients treated with tocilizumab ≤2 days after initiation of IMV had a similar risk of mortality (aHR 1.47; 95% CI 0.96 to 2.26), but significantly shorter time-to-extubation (aHR 0.37; 95% CI 0.23 to 0.58) and time-to-hospital-discharge (aHR 0.31; 95% CI CI 0.17 to 0.56) compared with patients treated with tocilizumab 3–10 days after initiation of IMV.ConclusionsAmong mechanically ventilated patients with COVID-19, the risk of death in the 28-day follow-up period was similar, but time-to-extubation and time-to-hospital-discharge were longer in patients who received tocilizumab within 10 days of initiation of IMV compared with patients who did not receive tocilizumab.

Job exposure matrices (JEMs) are tools used to classify exposures for job titles based on general job tasks in the absence of individual level data. However, exposure uncertainty due to variations in worker practices, job conditions, and the quality of data has never been quantified systematically in a JEM. We describe a methodology for creating a JEM which defines occupational exposures on a continuous scale and utilizes elicitation methods to quantify exposure uncertainty by assigning exposures probability distributions with parameters determined through expert involvement. Experts use their knowledge to develop mathematical models using related exposure surrogate data in the absence of available occupational level data and to adjust model output against other similar occupations. Formal expert elicitation methods provided a consistent, efficient process to incorporate expert judgment into a large, consensus-based JEM. A population-based electric shock JEM was created using these methods, allowing for transparent estimates of exposure.