Hacia un nuevo modelo explicativo para las ciencias especiales

Los filósofos postulan leyes putativas ceteris paribus para legitimar las explicaciones en las ciencias especiales. Este constructo se basa en el supuesto de que el modelo explicativo correcto en las ciencias especiales es un modelo de cobertura legal. Este artículo muestra que no existe un modelo de cobertura legal ceteris paribus que sea coherente. Defenderé que no es necesario llegar a la conclusión de que no existen explicaciones científicamente legítimas en las ciencias especiales,sino que hace falta un nuevo modelo explicativo que las incluya. Este artículo considera el modelo explicativo de dos niveles de Salmon como punto de partida para la formulación de dicho modelo.Philosophers postulate putative ceteris paribus laws to legitimise their explanations in the special sciences. This construct is based on the assumption that the correct explanatory model in the special sciences is a model with legal coverage. This article shows that there exists no coherent ceteris paribus model of legal coverage. I will uphold the position that it is not necessary to reach the conclusion that there are no scientifically legitimate explanations in the social sciences, rather that what is lacking is a new explanatory model that includes them. This article examines the explanatory model of two levels of Salmon as a point of departure for formulating this model

Parents’ perceptions of their children’s physical activity during the COVID-19 pandemic

Background COVID-19 has drastically changed the everyday lives of children, including limiting interactions with peers, loss of regularly organized activities, and closure of schools and recreational facilities. While COVID-19 protocols are in place to reduce viral transmission, they have affected children’s access to physical activity opportunities. The purpose of this study was to understand how COVID-19 has affected children’s engagement in physical activity and to identify strategies that can support children’s return to physical activity programming in public places. Methods Parents of past participants in the Grade 5 ACT-i-Pass Program in London, Ontario, Canada were invited to participate in a semi-structured interview online (in November and December 2020) via Microsoft Teams. The script was comprised of questions about their child’s physical activity levels (before, current, and anticipated following COVID-19), lifestyle changes due to COVID-19, and what service providers can do to assist children’s return to public programming. Interviews were transcribed in Microsoft Teams, reviewed by a member of the research team, and analyzed in NVivo 12 using thematic analysis. Results Twenty-seven parents participated in an interview. Four themes and two subthemes were identified during analysis: (1) modifications to everyday life (a. activity options available and b. altered social environment), (2) safety in public spaces, (3) accessibility of activities, and (4) utilizing outdoor spaces. Conclusions COVID-19 protocols have decreased children’s physical activity levels due to the loss of their regular activities, recreational spaces, and peer support. Implementing facility and activity-specific health protocols, providing outdoor activity options, and offering a variety of activity types, times, and locations are three strategies recommended by parents to help facilitate their children’s return to public recreational places. Due to the negative consequences of physical inactivity on children’s health and well-being, service providers need to implement programming and safety protocols that support children’s engagement in physical activity throughout the remainder of, and the years following, the COVID-19 pandemic

Fabrication and Qualitative Analysis of an Optical Fibre Efpi-Based Temperature Sensor

The following presents a comparison of an extrinsic Fabry–Perot interferometer (EFPI)- based temperature sensor, constructed using a novel diaphragm manufacturing technique, with a reference all-glass EFPI temperature sensor. The novel diaphragm was manufactured using polyvinyl alcohol (PVA). The novel sensor fabrication involved fusing a single-mode fibre (SMF) to a length of fused quartz capillary, which has an inner diameter of 132 µm and a 220 µm outer diameter. The capillary was subsequently polished until the distal face of the capillary extended approximately 60 µm beyond that of the single mode fibre. Upon completion of polishing, the assembly is immersed in a solution of PVA. Controlled extraction resulted in creation of a thin diaphragm while simultaneously applying a protective coating to the fusion point of the SMF and capillary. The EFPI sensor is subsequently sealed in a second fluid-filled capillary, thereby creating a novel temperature sensor structure. Both temperature sensors were placed in a thermogravimetric analyser and heated from an indicated 30 °C to 100 °C to qualitatively compare sensitivities. Initial results indicated that the novel manufacturing technique both expedited production and produces a more sensitive sensor when compared to an all-glass construction

A systematic review of natural language processing applied to radiology reports

NLP has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology reports. Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited. This study systematically assesses recent literature in NLP applied to radiology reports. Our automated literature search yields 4,799 results using automated filtering, metadata enriching steps and citation search combined with manual review. Our analysis is based on 21 variables including radiology characteristics, NLP methodology, performance, study, and clinical application characteristics. We present a comprehensive analysis of the 164 publications retrieved with each categorised into one of 6 clinical application categories. Deep learning use increases but conventional machine learning approaches are still prevalent. Deep learning remains challenged when data is scarce and there is little evidence of adoption into clinical practice. Despite 17% of studies reporting greater than 0.85 F1 scores, it is hard to comparatively evaluate these approaches given that most of them use different datasets. Only 14 studies made their data and 15 their code available with 10 externally validating results. Automated understanding of clinical narratives of the radiology reports has the potential to enhance the healthcare process but reproducibility and explainability of models are important if the domain is to move applications into clinical use. More could be done to share code enabling validation of methods on different institutional data and to reduce heterogeneity in reporting of study properties allowing inter-study comparisons. Our results have significance for researchers providing a systematic synthesis of existing work to build on, identify gaps, opportunities for collaboration and avoid duplication

The reporting quality of natural language processing studies - systematic review of studies of radiology reports

Abstract Background Automated language analysis of radiology reports using natural language processing (NLP) can provide valuable information on patients’ health and disease. With its rapid development, NLP studies should have transparent methodology to allow comparison of approaches and reproducibility. This systematic review aims to summarise the characteristics and reporting quality of studies applying NLP to radiology reports. Methods We searched Google Scholar for studies published in English that applied NLP to radiology reports of any imaging modality between January 2015 and October 2019. At least two reviewers independently performed screening and completed data extraction. We specified 15 criteria relating to data source, datasets, ground truth, outcomes, and reproducibility for quality assessment. The primary NLP performance measures were precision, recall and F1 score. Results Of the 4,836 records retrieved, we included 164 studies that used NLP on radiology reports. The commonest clinical applications of NLP were disease information or classification (28%) and diagnostic surveillance (27.4%). Most studies used English radiology reports (86%). Reports from mixed imaging modalities were used in 28% of the studies. Oncology (24%) was the most frequent disease area. Most studies had dataset size > 200 (85.4%) but the proportion of studies that described their annotated, training, validation, and test set were 67.1%, 63.4%, 45.7%, and 67.7% respectively. About half of the studies reported precision (48.8%) and recall (53.7%). Few studies reported external validation performed (10.8%), data availability (8.5%) and code availability (9.1%). There was no pattern of performance associated with the overall reporting quality. Conclusions There is a range of potential clinical applications for NLP of radiology reports in health services and research. However, we found suboptimal reporting quality that precludes comparison, reproducibility, and replication. Our results support the need for development of reporting standards specific to clinical NLP studies

Assessment of the molecular mechanisms of action of novel 4-phenylpyridine-2-one and 6-phenylpyrimidin-4-one allosteric modulators at the M1 muscarinic acetylcholine receptors

Positive allosteric modulators (PAMs) that target the M1 muscarinic acetylcholine (ACh) receptor (M1 mAChR) are potential treatments for cognitive deficits in conditions such as Alzheimer's disease and schizophrenia. We recently reported novel 4-phenylpyridine-2-one and 6-phenylpyrimidin-4-one M1 mAChR PAMs with the potential to display different modes of positive allosteric modulation and/or agonism (Mistry et al., 2016), but their molecular mechanisms of action remain undetermined. The current study compared the pharmacology of three such novel PAMs with the prototypical first-generation PAM, BQCA, in a recombinant Chinese hamster ovary (CHO) cell line stably expressing the human M1 mAChR. Interactions between the orthosteric agonists and the novel PAMs or BQCA suggested their allosteric effects were solely governed by modulation of agonist affinity. The greatest degree of positive co-operativity was observed with higher efficacy agonists, whereas minimal potentiation was observed when the modulators were tested against the lower efficacy agonist, xanomeline. Each PAM was investigated for its effects on the endogenous agonist, ACh, on three different signalling pathways, (ERK1/2 phosphorylation, IP1 accumulation and β-arrestin-2 recruitment), revealing that the allosteric potentiation generally tracked with the efficiency of stimulus-response coupling and that there was little pathway bias in the allosteric effects. Thus, despite the identification of novel allosteric scaffolds targeting the M1 mAChR, the molecular mechanism of action of these compounds is largely consistent with a model of allostery previously described for BQCA, suggesting that this may be a more generalized mechanism for M1 mAChR PAM effects than previously appreciated

Understanding the performance and reliability of NLP tools: a comparison of four NLP tools predicting stroke phenotypes in radiology reports

BACKGROUND: Natural language processing (NLP) has the potential to automate the reading of radiology reports, but there is a need to demonstrate that NLP methods are adaptable and reliable for use in real-world clinical applications. METHODS: We tested the F1 score, precision, and recall to compare NLP tools on a cohort from a study on delirium using images and radiology reports from NHS Fife and a population-based cohort (Generation Scotland) that spans multiple National Health Service health boards. We compared four off-the-shelf rule-based and neural NLP tools (namely, EdIE-R, ALARM+, ESPRESSO, and Sem-EHR) and reported on their performance for three cerebrovascular phenotypes, namely, ischaemic stroke, small vessel disease (SVD), and atrophy. Clinical experts from the EdIE-R team defined phenotypes using labelling techniques developed in the development of EdIE-R, in conjunction with an expert researcher who read underlying images. RESULTS: EdIE-R obtained the highest F1 score in both cohorts for ischaemic stroke, ≥93%, followed by ALARM+, ≥87%. The F1 score of ESPRESSO was ≥74%, whilst that of Sem-EHR is ≥66%, although ESPRESSO had the highest precision in both cohorts, 90% and 98%. For F1 scores for SVD, EdIE-R scored ≥98% and ALARM+ ≥90%. ESPRESSO scored lowest with ≥77% and Sem-EHR ≥81%. In NHS Fife, F1 scores for atrophy by EdIE-R and ALARM+ were 99%, dropping in Generation Scotland to 96% for EdIE-R and 91% for ALARM+. Sem-EHR performed lowest for atrophy at 89% in NHS Fife and 73% in Generation Scotland. When comparing NLP tool output with brain image reads using F1 scores, ALARM+ scored 80%, outperforming EdIE-R at 66% in ischaemic stroke. For SVD, EdIE-R performed best, scoring 84%, with Sem-EHR 82%. For atrophy, EdIE-R and both ALARM+ versions were comparable at 80%. CONCLUSIONS: The four NLP tools show varying F1 (and precision/recall) scores across all three phenotypes, although more apparent for ischaemic stroke. If NLP tools are to be used in clinical settings, this cannot be performed "out of the box." It is essential to understand the context of their development to assess whether they are suitable for the task at hand or whether further training, re-training, or modification is required to adapt tools to the target task

Understanding the performance and reliability of NLP tools:A comparison of four NLP tools predicting stroke phenotypes in radiology reports

Background: Natural language processing (NLP) has the potential to automate the reading of radiology reports, but there is a need to demonstrate that NLP methods are adaptable and reliable for use in real-world clinical applications. Methods: We tested the F1 score, precision, and recall to compare NLP tools on a cohort from a study on delirium using images and radiology reports from NHS Fife and a population-based cohort (Generation Scotland) that spans multiple National Health Service health boards. We compared four off-the-shelf rule-based and neural NLP tools (namely, EdIE-R, ALARM+, ESPRESSO, and Sem-EHR) and reported on their performance for three cerebrovascular phenotypes, namely, ischaemic stroke, small vessel disease (SVD), and atrophy. Clinical experts from the EdIE-R team defined phenotypes using labelling techniques developed in the development of EdIE-R, in conjunction with an expert researcher who read underlying images. Results: EdIE-R obtained the highest F1 score in both cohorts for ischaemic stroke, ≥93%, followed by ALARM+, ≥87%. The F1 score of ESPRESSO was ≥74%, whilst that of Sem-EHR is ≥66%, although ESPRESSO had the highest precision in both cohorts, 90% and 98%. For F1 scores for SVD, EdIE-R scored ≥98% and ALARM+ ≥90%. ESPRESSO scored lowest with ≥77% and Sem-EHR ≥81%. In NHS Fife, F1 scores for atrophy by EdIE-R and ALARM+ were 99%, dropping in Generation Scotland to 96% for EdIE-R and 91% for ALARM+. Sem-EHR performed lowest for atrophy at 89% in NHS Fife and 73% in Generation Scotland. When comparing NLP tool output with brain image reads using F1 scores, ALARM+ scored 80%, outperforming EdIE-R at 66% in ischaemic stroke. For SVD, EdIE-R performed best, scoring 84%, with Sem-EHR 82%. For atrophy, EdIE-R and both ALARM+ versions were comparable at 80%. Conclusions: The four NLP tools show varying F1 (and precision/recall) scores across all three phenotypes, although more apparent for ischaemic stroke. If NLP tools are to be used in clinical settings, this cannot be performed “out of the box.” It is essential to understand the context of their development to assess whether they are suitable for the task at hand or whether further training, re-training, or modification is required to adapt tools to the target task

Public health professionals' perceptions toward provision of health protection in England: a survey of expectations of Primary Care Trusts and Health Protection Units in the delivery of health protection

BACKGROUND: Effective health protection requires systematised responses with clear accountabilities. In England, Primary Care Trusts and the Health Protection Agency both have statutory responsibilities for health protection. A Memorandum of Understanding identifies responsibilities of both parties, but there is a potential lack of clarity about responsibility for specific health protection functions. We aimed to investigate professionals' perceptions of responsibility for different health protection functions, to inform future guidance for, and organisation of, health protection in England. METHODS: We sent a postal questionnaire to all health protection professionals in England from the following groups: (a) Directors of Public Health in Primary Care Trusts; (b) Directors of Health Protection Units within the Health Protection Agency; (c) Directors of Public Health in Strategic Health Authorities and; (d) Regional Directors of the Health Protection Agency RESULTS: The response rate exceeded 70%. Variations in perceptions of who should be, and who is, delivering health protection functions were observed within, and between, the professional groups (a)-(d). Concordance in views of which organisation should, and which does deliver was high (≥90%) for 6 of 18 health protection functions, but much lower (≤80%) for 6 other functions, including managing the implications of a case of meningitis out of hours, of landfill environmental contamination, vaccination in response to mumps outbreaks, nursing home infection control, monitoring sexually transmitted infections and immunisation training for primary care staff. The proportion of respondents reporting that they felt confident most or all of the time in the safe delivery of a health protection function was strongly correlated with the concordance (r = 0.65, P = 0.0038). CONCLUSION: Whilst we studied professionals' perceptions, rather than actual responses to incidents, our study suggests that there are important areas of health protection where consistent understanding of responsibility for delivery is lacking. There are opportunities to clarify the responsibility for health protection in England, perhaps learning from the approaches used for those health protection functions where we found consistent perceptions of accountability

Children's School Lives in Junior Infants

This report is the third in the series from Children’s School Lives, an innovative, longitudinal research study involving almost 4,000 children in 189 primary schools. One of the defining features of the study is the strong emphasis it places on listening to and learning directly from children about their experience of being in primary school in Ireland. This particular report introduces us to the youngest children in the study. The multiple perspectives gathered from the children themselves, their families, teachers and school principals, converge to provide us with a rich, detailed picture of the children’s first year in school. Uniquely, this period incorporates the months just prior to the arrival of the Coronavirus on Irish shores and the weeks immediately after the commencement of the first national lockdown in Spring 2020. Early childhood is a time of being and becoming, a time which provides important foundations for children’s learning and for life itself. We know from research that the first six years of a child’s life, their early childhood years, are particularly important for their holistic development. We also know from research that a positive transition from preschool to primary school is a predictor of children’s future success in terms of social, emotional and educational outcomes. Yet, despite this knowledge, relatively little research exists in the Irish context on children’s initial experiences in primary school. The Children’s School Lives study responds directly to this research gap by capturing, through multiple voices, comprehensive insights into the children’s initial weeks and months in their primary classrooms.National Council for Curriculum and Assessment (NCCA