Defining sepsis on the wards: results of a multi-centre point-prevalence study comparing two sepsis definitions

Our aim was to prospectively determine the predictive capabilities of SEPSIS-1 and SEPSIS-3 definitions in the emergency departments and general wards. Patients with National Early Warning Score (NEWS) of 3 or above and suspected or proven infection were enrolled over a 24-h period in 13 Welsh hospitals. The primary outcome measure was mortality within 30 days. Out of the 5422 patients screened, 431 fulfilled inclusion criteria and 380 (88%) were recruited. Using the SEPSIS-1 definition, 212 patients had sepsis. When using the SEPSIS-3 definitions with Sequential Organ Failure Assessment (SOFA) score ≥ 2, there were 272 septic patients, whereas with quickSOFA score ≥ 2, 50 patients were identified. For the prediction of primary outcome, SEPSIS-1 criteria had a sensitivity (95%CI) of 65% (54–75%) and specificity of 47% (41–53%); SEPSIS-3 criteria had a sensitivity of 86% (76–92%) and specificity of 32% (27–38%). SEPSIS-3 and SEPSIS-1 definitions were associated with a hazard ratio (95%CI) 2.7 (1.5–5.6) and 1.6 (1.3–2.5), respectively. Scoring system discrimination evaluated by receiver operating characteristic curves was highest for Sequential Organ Failure Assessment score (0.69 (95%CI 0.63–0.76)), followed by NEWS (0.58 (0.51–0.66)) (p < 0.001). Systemic inflammatory response syndrome criteria (0.55 (0.49–0.61)) and quickSOFA score (0.56 (0.49–0.64)) could not predict outcome. The SEPSIS-3 definition identified patients with the highest risk. Sequential Organ Failure Assessment score and NEWS were better predictors of poor outcome. The Sequential Organ Failure Assessment score appeared to be the best tool for identifying patients with high risk of death and sepsis-induced organ dysfunction

General anaesthetic and airway management practice for obstetric surgery in England: a prospective, multi-centre observational study

There are no current descriptions of general anaesthesia characteristics for obstetric surgery, despite recent changes to patient baseline characteristics and airway management guidelines. This analysis of data from the direct reporting of awareness in maternity patients' (DREAMY) study of accidental awareness during obstetric anaesthesia aimed to describe practice for obstetric general anaesthesia in England and compare with earlier surveys and best-practice recommendations. Consenting patients who received general anaesthesia for obstetric surgery in 72 hospitals from May 2017 to August 2018 were included. Baseline characteristics, airway management, anaesthetic techniques and major complications were collected. Descriptive analysis, binary logistic regression modelling and comparisons with earlier data were conducted. Data were collected from 3117 procedures, including 2554 (81.9%) caesarean deliveries. Thiopental was the induction drug in 1649 (52.9%) patients, compared with propofol in 1419 (45.5%). Suxamethonium was the neuromuscular blocking drug for tracheal intubation in 2631 (86.1%), compared with rocuronium in 367 (11.8%). Difficult tracheal intubation was reported in 1 in 19 (95%CI 1 in 16-22) and failed intubation in 1 in 312 (95%CI 1 in 169-667). Obese patients were over-represented compared with national baselines and associated with difficult, but not failed intubation. There was more evidence of change in practice for induction drugs (increased use of propofol) than neuromuscular blocking drugs (suxamethonium remains the most popular). There was evidence of improvement in practice, with increased monitoring and reversal of neuromuscular blockade (although this remains suboptimal). Despite a high risk of difficult intubation in this population, videolaryngoscopy was rarely used (1.9%)

Face Recognition Vendor Test 2002 Performance Metrics

Audio-Visual Based Person Authentication, in June 2003. The paper details the metrics used to quantify the performance of the face recognition systems tested in FRVT 2002. The methods are suited to any recognition evaluation, online or offline, technology or scenario, for which complete similarity scores are archived. The paper shows that the open-set identification problem known as the watch list task is the general case: it requires systems to perform 1:N recognition with concurrent possibilities of false acceptance and rejection. Two special cases are demonstrated: 1:1 Verification is simply the watch list task with N=1; and closed-set identification is that with no false accept rate. The paper also presents the computation of standard error ellipses used to show the effect of population variation on false accept and false reject rates

The NIST Human ID Evaluation Framework

Abstract. The NIST HumanID Evaluation Framework, or HEF, is an effort to design, implement, and deploy standards for the robust and complete documentation of the biometric system evaluation process. The HEF is leverages contemporary technologies, specifically XML, for the formal description of biometric tests. The HEF was used to facilitate the administration of the Face Recognition Vendor Test (FRVT) 2002. Unlike FRVT 2000 or the FERET 1996 evaluations, FRVT 2002 used a large number (over 100,000) of both still and video facial imagery, warranting the development of a more sophisticated and regular means of describing data presented to the participants. The HEF is one component in NIST’s ongoing effort to address the need in the biometrics community for a common evaluation framework.

Binary Decision Clustering for Neural Network Based OCR

This paper presents a clusterin

How features of the human face affect recognition: A statistical comparison of three face recognition algorithms

Recognition difficulty is statistically linked to ¡¢ ¡ subject covariate factors such as age and gender for three face recognition algorithms: principle components analysis, an interpersonal image difference classifier, and an elastic bunch graph matching algorithm. The covariates assess race, gender, age, glasses use, facial hair, bangs, mouth state, complexion, state of eyes, makeup use, and facial expression. We use two statistical models. First, an ANOVA relates covariates to normalized similarity scores. Second, logistic regression relates subject covariates to probability of rank one recognition. These models have strong explanatory power as measured by £¥ ¤ and deviance reduction, while providing complementary and corroborative results. Some factors, like changes to the eye status, affect all algorithms similarly. Other factors, such as race, affect different algorithms differently. Tabular and graphical summaries of results provide a wealth of empirical evidence. Plausible explanations of many results can be motivated from knowledge of the algorithms. Other results are surprising and suggest a need for further study.