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Détail de l'auteur
Auteur Matthew S. GOODWIN |
Documents disponibles écrits par cet auteur (5)



Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises / Daniel BONE in Journal of Autism and Developmental Disorders, 45-5 (May 2015)
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Titre : Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises Type de document : Texte imprimé et/ou numérique Auteurs : Daniel BONE, Auteur ; Matthew S. GOODWIN, Auteur ; Matthew P. BLACK, Auteur ; Chi-Chun LEE, Auteur ; Kartik AUDHKHASI, Auteur ; Shrikanth NARAYANAN, Auteur Article en page(s) : p.1121-1136 Langues : Anglais (eng) Mots-clés : Autism diagnostic observation schedule Autism diagnostic interview Machine learning Signal processing Autism Diagnosis Index. décimale : PER Périodiques Résumé : Machine learning has immense potential to enhance diagnostic and intervention research in the behavioral sciences, and may be especially useful in investigations involving the highly prevalent and heterogeneous syndrome of autism spectrum disorder. However, use of machine learning in the absence of clinical domain expertise can be tenuous and lead to misinformed conclusions. To illustrate this concern, the current paper critically evaluates and attempts to reproduce results from two studies (Wall et al. in Transl Psychiatry 2(4):e100, 2012a; PloS One 7(8), 2012b) that claim to drastically reduce time to diagnose autism using machine learning. Our failure to generate comparable findings to those reported by Wall and colleagues using larger and more balanced data underscores several conceptual and methodological problems associated with these studies. We conclude with proposed best-practices when using machine learning in autism research, and highlight some especially promising areas for collaborative work at the intersection of computational and behavioral science. En ligne : http://dx.doi.org/10.1007/s10803-014-2268-6 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=259
in Journal of Autism and Developmental Disorders > 45-5 (May 2015) . - p.1121-1136[article] Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises [Texte imprimé et/ou numérique] / Daniel BONE, Auteur ; Matthew S. GOODWIN, Auteur ; Matthew P. BLACK, Auteur ; Chi-Chun LEE, Auteur ; Kartik AUDHKHASI, Auteur ; Shrikanth NARAYANAN, Auteur . - p.1121-1136.
Langues : Anglais (eng)
in Journal of Autism and Developmental Disorders > 45-5 (May 2015) . - p.1121-1136
Mots-clés : Autism diagnostic observation schedule Autism diagnostic interview Machine learning Signal processing Autism Diagnosis Index. décimale : PER Périodiques Résumé : Machine learning has immense potential to enhance diagnostic and intervention research in the behavioral sciences, and may be especially useful in investigations involving the highly prevalent and heterogeneous syndrome of autism spectrum disorder. However, use of machine learning in the absence of clinical domain expertise can be tenuous and lead to misinformed conclusions. To illustrate this concern, the current paper critically evaluates and attempts to reproduce results from two studies (Wall et al. in Transl Psychiatry 2(4):e100, 2012a; PloS One 7(8), 2012b) that claim to drastically reduce time to diagnose autism using machine learning. Our failure to generate comparable findings to those reported by Wall and colleagues using larger and more balanced data underscores several conceptual and methodological problems associated with these studies. We conclude with proposed best-practices when using machine learning in autism research, and highlight some especially promising areas for collaborative work at the intersection of computational and behavioral science. En ligne : http://dx.doi.org/10.1007/s10803-014-2268-6 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=259 Mapping the time course of overt emotion dysregulation, self-injurious behavior, and aggression in psychiatrically hospitalized autistic youth: A naturalistic study / Jessie B. NORTHRUP in Autism Research, 15-10 (October 2022)
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Titre : Mapping the time course of overt emotion dysregulation, self-injurious behavior, and aggression in psychiatrically hospitalized autistic youth: A naturalistic study Type de document : Texte imprimé et/ou numérique Auteurs : Jessie B. NORTHRUP, Auteur ; Matthew S. GOODWIN, Auteur ; Christine B. PEURA, Auteur ; Qi CHEN, Auteur ; Briana J. TAYLOR, Auteur ; Matthew S. SIEGEL, Auteur ; Carla A. MAZEFSKY, Auteur Article en page(s) : p.1855-1867 Langues : Anglais (eng) Mots-clés : aggressive behavior autism inpatient collection autism spectrum disorder emotion dysregulation self-injurious behavior Index. décimale : PER Périodiques Résumé : Challenges with emotion dysregulation, self-injurious behavior (SIB), and aggression are common in autistic individuals. Prior research on the relationships between these behaviors is limited mainly to cross-sectional correlations of parent-report data. Understanding how emotion dysregulation, SIB, and aggression present and relate to one another in real-time could add to our understanding of the context and function of these behaviors. The present study examined the real-time occurrence and temporal relationships between these behaviors in 53 psychiatrically hospitalized autistic youth. Over 500 hours of behavioral observation occurred during everyday activities in the hospital. Start and stop times for instances of overt emotion dysregulation, SIB, and aggression were coded live using a custom mobile phone app. Results indicated large individual variability in the frequency and duration of these behaviors and their co-occurrence. Both SIB and aggression co-occurred with overt emotion dysregulation at above-chance levels, suggesting a role for emotional distress in the occurrence of these behaviors. However, there was substantial variability within and between individuals in co-occurrence, and SIB and aggression often (and for some individuals, almost always) occurred without overt emotion dysregulation. Relatedly, cross-recurrence quantitative analysis revealed that SIB and aggression preceded emotion dysregulation more often than emotion dysregulation preceded SIB and aggression. Future research, perhaps using ambulatory psychophysiological measures, is needed to understand whether emotion dysregulation may sometimes be present but not easily observed during SIB and aggression. LAY SUMMARY: This study provides insight into how overt emotion dysregulation (i.e., visible distress), aggression, and self-injury unfold in real-time for autistic individuals. Participants were 53 autistic youth staying in a psychiatric hospital. Research staff observed participants in everyday activities on the hospital unit and noted instances of aggression, self-injurious behavior, and emotion dysregulation. Results suggest that aggression and self-injury sometimes occur with visible signs of distress but also often occur without visible distress. In addition, observable distress was more common in the moments after these behaviors than in the moments before. En ligne : http://dx.doi.org/10.1002/aur.2773 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=488
in Autism Research > 15-10 (October 2022) . - p.1855-1867[article] Mapping the time course of overt emotion dysregulation, self-injurious behavior, and aggression in psychiatrically hospitalized autistic youth: A naturalistic study [Texte imprimé et/ou numérique] / Jessie B. NORTHRUP, Auteur ; Matthew S. GOODWIN, Auteur ; Christine B. PEURA, Auteur ; Qi CHEN, Auteur ; Briana J. TAYLOR, Auteur ; Matthew S. SIEGEL, Auteur ; Carla A. MAZEFSKY, Auteur . - p.1855-1867.
Langues : Anglais (eng)
in Autism Research > 15-10 (October 2022) . - p.1855-1867
Mots-clés : aggressive behavior autism inpatient collection autism spectrum disorder emotion dysregulation self-injurious behavior Index. décimale : PER Périodiques Résumé : Challenges with emotion dysregulation, self-injurious behavior (SIB), and aggression are common in autistic individuals. Prior research on the relationships between these behaviors is limited mainly to cross-sectional correlations of parent-report data. Understanding how emotion dysregulation, SIB, and aggression present and relate to one another in real-time could add to our understanding of the context and function of these behaviors. The present study examined the real-time occurrence and temporal relationships between these behaviors in 53 psychiatrically hospitalized autistic youth. Over 500 hours of behavioral observation occurred during everyday activities in the hospital. Start and stop times for instances of overt emotion dysregulation, SIB, and aggression were coded live using a custom mobile phone app. Results indicated large individual variability in the frequency and duration of these behaviors and their co-occurrence. Both SIB and aggression co-occurred with overt emotion dysregulation at above-chance levels, suggesting a role for emotional distress in the occurrence of these behaviors. However, there was substantial variability within and between individuals in co-occurrence, and SIB and aggression often (and for some individuals, almost always) occurred without overt emotion dysregulation. Relatedly, cross-recurrence quantitative analysis revealed that SIB and aggression preceded emotion dysregulation more often than emotion dysregulation preceded SIB and aggression. Future research, perhaps using ambulatory psychophysiological measures, is needed to understand whether emotion dysregulation may sometimes be present but not easily observed during SIB and aggression. LAY SUMMARY: This study provides insight into how overt emotion dysregulation (i.e., visible distress), aggression, and self-injury unfold in real-time for autistic individuals. Participants were 53 autistic youth staying in a psychiatric hospital. Research staff observed participants in everyday activities on the hospital unit and noted instances of aggression, self-injurious behavior, and emotion dysregulation. Results suggest that aggression and self-injury sometimes occur with visible signs of distress but also often occur without visible distress. In addition, observable distress was more common in the moments after these behaviors than in the moments before. En ligne : http://dx.doi.org/10.1002/aur.2773 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=488 Regulating Together: Emotion Dysregulation Group Treatment for ASD Youth and Their Caregivers / Rebecca C. SHAFFER in Journal of Autism and Developmental Disorders, 53-5 (May 2023)
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Titre : Regulating Together: Emotion Dysregulation Group Treatment for ASD Youth and Their Caregivers Type de document : Texte imprimé et/ou numérique Auteurs : Rebecca C. SHAFFER, Auteur ; Lauren M. SCHMITT, Auteur ; Debra L. REISINGER, Auteur ; Marika COFFMAN, Auteur ; Paul HORN, Auteur ; Matthew S. GOODWIN, Auteur ; Carla MAZEFSKY, Auteur ; Shelley RANDALL, Auteur ; Craig ERICKSON, Auteur Article en page(s) : p.1942-1962 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : Individuals with autism spectrum disorder (ASD) experience behavioral and emotional symptoms hypothesized to arise from emotion dysregulation (ED), difficulty modulating emotional experience, expression, and intensity in an acceptable and contextually appropriate manner. We developed Regulating Together (RT)-an intensive-outpatient, caregiver-assisted group program to meet the ASD?+?ED intervention critical need. A within-subjects trial was conducted (5-week-control lead-in period, 5-week-treatment, and 5-and 10-weeks-post-treatment follow-ups). Forty-four youth with ASD?+?ED (25 8-12, 19 13-18 yr-olds, 88% male, mean FSIQ of 96) participated. Improvements were found in reactivity, emotion regulation knowledge, and flexibility post-treatment and 10-weeks post-treatment. A reduction in inpatient hospitalization rates by 16% from the 12 months pre-RT to 12 months post-RT was observed. RT shows promise to reduce ED in ASD. En ligne : https://doi.org/10.1007/s10803-022-05461-x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=501
in Journal of Autism and Developmental Disorders > 53-5 (May 2023) . - p.1942-1962[article] Regulating Together: Emotion Dysregulation Group Treatment for ASD Youth and Their Caregivers [Texte imprimé et/ou numérique] / Rebecca C. SHAFFER, Auteur ; Lauren M. SCHMITT, Auteur ; Debra L. REISINGER, Auteur ; Marika COFFMAN, Auteur ; Paul HORN, Auteur ; Matthew S. GOODWIN, Auteur ; Carla MAZEFSKY, Auteur ; Shelley RANDALL, Auteur ; Craig ERICKSON, Auteur . - p.1942-1962.
Langues : Anglais (eng)
in Journal of Autism and Developmental Disorders > 53-5 (May 2023) . - p.1942-1962
Index. décimale : PER Périodiques Résumé : Individuals with autism spectrum disorder (ASD) experience behavioral and emotional symptoms hypothesized to arise from emotion dysregulation (ED), difficulty modulating emotional experience, expression, and intensity in an acceptable and contextually appropriate manner. We developed Regulating Together (RT)-an intensive-outpatient, caregiver-assisted group program to meet the ASD?+?ED intervention critical need. A within-subjects trial was conducted (5-week-control lead-in period, 5-week-treatment, and 5-and 10-weeks-post-treatment follow-ups). Forty-four youth with ASD?+?ED (25 8-12, 19 13-18 yr-olds, 88% male, mean FSIQ of 96) participated. Improvements were found in reactivity, emotion regulation knowledge, and flexibility post-treatment and 10-weeks post-treatment. A reduction in inpatient hospitalization rates by 16% from the 12 months pre-RT to 12 months post-RT was observed. RT shows promise to reduce ED in ASD. En ligne : https://doi.org/10.1007/s10803-022-05461-x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=501
Titre : Technology Tools for Students with Autism : Innovations that Enhance Independence and Learning Type de document : Texte imprimé et/ou numérique Auteurs : Katharina I. BOSER, Directeur de publication ; Matthew S. GOODWIN, Directeur de publication ; Sarah C. WAYLAND, Directeur de publication ; John Elder ROBISON, Préfacier, etc. ; Geraldine DAWSON, Préfacier, etc. Editeur : Baltimore [Etats-Unis] : Brookes Publishing Année de publication : 2014 Importance : 335 p. Présentation : ill. Format : 17,7cm x 25,5cm x 2cm ISBN/ISSN/EAN : 978-1-59857-262-9 Note générale : Bibliogr. Index Langues : Anglais (eng) Mots-clés : Réalité virtuelle Technologie mobile Index. décimale : EDU-M EDU-M - Education - Technologies Résumé : Technology holds great promise for helping students with autism learn, communicate, and function effectively in the modern world. Start leveraging that power today with this forward-thinking book, your in-depth guided tour of technologies that support learners with autism and help them fully participate in their classroom and community. You'll learn about readily available technologies you can use right now—from apps to video modeling—and explore next-wave innovations that will help shape the future of autism intervention, such as therapeutic robots and advanced virtual reality technologies. You'll also get critical guidance on how to select the appropriate technology for your needs, weave technology into a universal design for learning framework, and conduct effective professional development so teachers make the most of new tools and strategies.
Discover Technologies That Help
•support the overall learning of children on the autism spectrum
•teach social skills and support emotion regulation through independent data collection
•develop executive function strategies and improve flexibility, memory, and transitions
•boost literacy and language skills
•support young adults' transition to the workplace
•make data collection and program evaluation more effective and efficient
•strengthen teacher training programs
•enhance use of evidence-based practices
Explore the benefits of technologies like
- apps for education, communication, behavior regulation, and more
- video modeling
- language processing software
- customized digital stories and book creator apps
- element cue supports
- emotional regulation and sensing technologies
- interactive learning software to improve feedback and metacognition
- visualization and mind mapping apps
- text-to-speech and speech to text software
- e-readers and tablets with integrated multimedia (e.g., cameras, microphones, etc.)
- electronic data collection forms for use with handheld devices
- and more [Résumé d'Auteur/Editeur]Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=266 Technology Tools for Students with Autism : Innovations that Enhance Independence and Learning [Texte imprimé et/ou numérique] / Katharina I. BOSER, Directeur de publication ; Matthew S. GOODWIN, Directeur de publication ; Sarah C. WAYLAND, Directeur de publication ; John Elder ROBISON, Préfacier, etc. ; Geraldine DAWSON, Préfacier, etc. . - Baltimore [Etats-Unis] : Brookes Publishing, 2014 . - 335 p. : ill. ; 17,7cm x 25,5cm x 2cm.
ISBN : 978-1-59857-262-9
Bibliogr. Index
Langues : Anglais (eng)
Mots-clés : Réalité virtuelle Technologie mobile Index. décimale : EDU-M EDU-M - Education - Technologies Résumé : Technology holds great promise for helping students with autism learn, communicate, and function effectively in the modern world. Start leveraging that power today with this forward-thinking book, your in-depth guided tour of technologies that support learners with autism and help them fully participate in their classroom and community. You'll learn about readily available technologies you can use right now—from apps to video modeling—and explore next-wave innovations that will help shape the future of autism intervention, such as therapeutic robots and advanced virtual reality technologies. You'll also get critical guidance on how to select the appropriate technology for your needs, weave technology into a universal design for learning framework, and conduct effective professional development so teachers make the most of new tools and strategies.
Discover Technologies That Help
•support the overall learning of children on the autism spectrum
•teach social skills and support emotion regulation through independent data collection
•develop executive function strategies and improve flexibility, memory, and transitions
•boost literacy and language skills
•support young adults' transition to the workplace
•make data collection and program evaluation more effective and efficient
•strengthen teacher training programs
•enhance use of evidence-based practices
Explore the benefits of technologies like
- apps for education, communication, behavior regulation, and more
- video modeling
- language processing software
- customized digital stories and book creator apps
- element cue supports
- emotional regulation and sensing technologies
- interactive learning software to improve feedback and metacognition
- visualization and mind mapping apps
- text-to-speech and speech to text software
- e-readers and tablets with integrated multimedia (e.g., cameras, microphones, etc.)
- electronic data collection forms for use with handheld devices
- and more [Résumé d'Auteur/Editeur]Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=266 Contenu
- What Is Driving Innovative and Assistive Technology Solutions in Autism Services? / Tracy GRAY
- Universal Design for Learning: Meeting the Needs of Learners with Autism Spectrum Disorders / Yvonne DOMINGS
- Classroom-Based Technology Tools / Christopher R. BUGAJ
- Using Virtual Reality Technology to Support the Learning of Children on the Autism Spectrum / Sarah PARSONS
- Language Software for Teaching Semantics, Grammar, and Pragmatics to Students with Autism / Katharine P. BEALS
- Mobile Media Devices: A Paradigm Shift in Communication Technology for Persons with Autism Spectrum Disorder / Jessica GOSNELL CARON
- Using Therapeutic Robots to Teach Students with Autism in the Classroom: Exploring Research and Innovation / Katharina I. BOSER
- Technology to Support Literacy in Autism / Sarah C. WAYLAND
- Using New Technology to Teach Emotion Recognition to Children with Autism Spectrum Disorders / Simon BARON-COHEN
- Incorporating Technology into Peer Social Group Programs / Andrea TARTARO
- Technologies to Support Interventions for Social-Emotional Intelligence, Self-Awareness, Personal Style, and Self-Regulation / Dorothy LUCCI
- No More Clipboards! Mobile Electronic Solutions for Data Collection, Behavior Analysis, and Self-Management Interventions / Minna LEVINE
- Racing Through the Professional-Development Obstacle Course / Christopher R. BUGAJ
- Using Distance Learning Technology to Increase Dissemination of Evidence-Based Practice in Autism Spectrum Disorder / Brooke R. INGERSOLL
- Bringing a School up to Speed: Experiences and Recommendations for Technology Implementation / Monica ADLER WERNER
- Tools to Support Simplified Capture of Activities in Natural Environments / Gregory D. ABOWD
- Using Mobile Technologies to Support Students in Work-Transition Programs / Gillian R. HAYES
Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité DOC0003356 EDU-M BOS Livre Centre d'Information et de Documentation du CRA Rhône-Alpes EDU - Education - Pédagogie Disponible Les abonnés qui ont emprunté ce document ont également emprunté :
The Neuroscience of Autism Spectrum Disorders BUXBAUM, Joseph D. Accompagner et prendre soin en institution médico-sociale DEMATEIS, Claude Autisme : l'accès aux apprentissages LENFANT, Anne Yvonne Rituels pour développer les compétences sociales et émotionnelles REYNAUD, Laure Autisme et intégration GRUBAR, Jean-Claude Les troubles autistiques LAZARTIGUES, Alain Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion / Daniel BONE in Journal of Child Psychology and Psychiatry, 57-8 (August 2016)
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Titre : Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion Type de document : Texte imprimé et/ou numérique Auteurs : Daniel BONE, Auteur ; Somer L. BISHOP, Auteur ; Matthew P. BLACK, Auteur ; Matthew S. GOODWIN, Auteur ; Catherine LORD, Auteur ; Shrikanth S. NARAYANAN, Auteur Article en page(s) : p.927-937 Langues : Anglais (eng) Mots-clés : Autism screening diagnosis machine learning Index. décimale : PER Périodiques Résumé : Background Machine learning (ML) provides novel opportunities for human behavior research and clinical translation, yet its application can have noted pitfalls (Bone et al., 2015). In this work, we fastidiously utilize ML to derive autism spectrum disorder (ASD) instrument algorithms in an attempt to improve upon widely used ASD screening and diagnostic tools. Methods The data consisted of Autism Diagnostic Interview-Revised (ADI-R) and Social Responsiveness Scale (SRS) scores for 1,264 verbal individuals with ASD and 462 verbal individuals with non-ASD developmental or psychiatric disorders, split at age 10. Algorithms were created via a robust ML classifier, support vector machine, while targeting best-estimate clinical diagnosis of ASD versus non-ASD. Parameter settings were tuned in multiple levels of cross-validation. Results The created algorithms were more effective (higher performing) than the current algorithms, were tunable (sensitivity and specificity can be differentially weighted), and were more efficient (achieving near-peak performance with five or fewer codes). Results from ML-based fusion of ADI-R and SRS are reported. We present a screener algorithm for below (above) age 10 that reached 89.2% (86.7%) sensitivity and 59.0% (53.4%) specificity with only five behavioral codes. Conclusions En ligne : http://dx.doi.org/10.1111/jcpp.12559 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=292
in Journal of Child Psychology and Psychiatry > 57-8 (August 2016) . - p.927-937[article] Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion [Texte imprimé et/ou numérique] / Daniel BONE, Auteur ; Somer L. BISHOP, Auteur ; Matthew P. BLACK, Auteur ; Matthew S. GOODWIN, Auteur ; Catherine LORD, Auteur ; Shrikanth S. NARAYANAN, Auteur . - p.927-937.
Langues : Anglais (eng)
in Journal of Child Psychology and Psychiatry > 57-8 (August 2016) . - p.927-937
Mots-clés : Autism screening diagnosis machine learning Index. décimale : PER Périodiques Résumé : Background Machine learning (ML) provides novel opportunities for human behavior research and clinical translation, yet its application can have noted pitfalls (Bone et al., 2015). In this work, we fastidiously utilize ML to derive autism spectrum disorder (ASD) instrument algorithms in an attempt to improve upon widely used ASD screening and diagnostic tools. Methods The data consisted of Autism Diagnostic Interview-Revised (ADI-R) and Social Responsiveness Scale (SRS) scores for 1,264 verbal individuals with ASD and 462 verbal individuals with non-ASD developmental or psychiatric disorders, split at age 10. Algorithms were created via a robust ML classifier, support vector machine, while targeting best-estimate clinical diagnosis of ASD versus non-ASD. Parameter settings were tuned in multiple levels of cross-validation. Results The created algorithms were more effective (higher performing) than the current algorithms, were tunable (sensitivity and specificity can be differentially weighted), and were more efficient (achieving near-peak performance with five or fewer codes). Results from ML-based fusion of ADI-R and SRS are reported. We present a screener algorithm for below (above) age 10 that reached 89.2% (86.7%) sensitivity and 59.0% (53.4%) specificity with only five behavioral codes. Conclusions En ligne : http://dx.doi.org/10.1111/jcpp.12559 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=292