Commentary: Holy grails, personalized medicine, and the public health burden of psychopathology ? a reflection on Ahuvia et al. (2023) / Lorenzo LORENZO-LUACES in Journal of Child Psychology and Psychiatry, 65-2 (February 2024)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 65-2 (February 2024) . - p.248-250 Titre : Commentary: Holy grails, personalized medicine, and the public health burden of psychopathology ? a reflection on Ahuvia et al. (2023) Type de document : Texte imprimé et/ou numérique Auteurs : Lorenzo LORENZO-LUACES, Auteur Article en page(s) : p.248-250 Index. décimale : PER Périodiques Résumé : Clinical psychology and psychiatry have many ?holy grails? or research findings that are widely sought after but remain elusive. The use of machine learning (ML) models for treatment selection is one of these holy grails. Ahuvia et al. (Journal of Child Psychology and Psychiatry, 2023) recently analyzed a large trial (n?=?996) of two distinct single-session interventions (SSIs) for internalizing distress and found little evidence that an ML model could predict differential treatment response. I discuss potential avenues for advancing SSI research. One avenue is the dissemination and implementation of SSIs, including how they interact with other treatments in routine care. Quantifying and critically questioning the promises of holy grails like ML models is sorely needed. Using simulation modeling to evaluate the relative merits of using ML models for treatment selection or using SSIs versus other treatment strategies may be another path forward. En ligne : https://doi.org/10.1111/jcpp.13914 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=520 [article] Commentary: Holy grails, personalized medicine, and the public health burden of psychopathology ? a reflection on Ahuvia et al. (2023) [Texte imprimé et/ou numérique] / Lorenzo LORENZO-LUACES, Auteur . - p.248-250. in Journal of Child Psychology and Psychiatry > 65-2 (February 2024) . - p.248-250 Index. décimale : PER Périodiques Résumé : Clinical psychology and psychiatry have many ?holy grails? or research findings that are widely sought after but remain elusive. The use of machine learning (ML) models for treatment selection is one of these holy grails. Ahuvia et al. (Journal of Child Psychology and Psychiatry, 2023) recently analyzed a large trial (n?=?996) of two distinct single-session interventions (SSIs) for internalizing distress and found little evidence that an ML model could predict differential treatment response. I discuss potential avenues for advancing SSI research. One avenue is the dissemination and implementation of SSIs, including how they interact with other treatments in routine care. Quantifying and critically questioning the promises of holy grails like ML models is sorely needed. Using simulation modeling to evaluate the relative merits of using ML models for treatment selection or using SSIs versus other treatment strategies may be another path forward. En ligne : https://doi.org/10.1111/jcpp.13914 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=520

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