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Enregistrement W2097405 · doi:10.1177/070674371405901002

How Does Untreated Psychosis Lead to Neurological Damage?

2014· editorial· en· W2097405 sur OpenAlexafffundvenue
Kwame McKenzie

Notice bibliographique

RevueThe Canadian Journal of Psychiatry · 2014
Typeeditorial
Langueen
DomaineMedicine
ThématiqueSchizophrenia research and treatment
Établissements canadiensUniversity of TorontoCentre for Addiction and Mental Health
Organismes subventionnairesNational Institute of Mental HealthCanadian Institutes of Health ResearchNational Institutes of Health
Mots-clésProdromePsychiatrySchizophrenia (object-oriented programming)PsychosisMedicineDiseaseIntervention (counseling)EtiologyIntensive care medicinePathology

Résumé

récupéré en direct d'OpenAlex

The idea that early treatment leads to better outcomes is a standard in medicine. From cancer to coronaries, we find that detection early in the disease course offers better prognosis. The longer a pathological process is left unchecked, the more damage is done; illnesses become more complex, thus they become more difficult to treat. In chronic diseases, such as diabetes, which have multifactorial etiologies, understanding the pathological process has allowed us to try to prevent illness by decreasing exposure to factors that increase risk and by screening for early signs of disease. It has also allowed us to offer treatment that can improve longevity. We have found that delay in treatment leads to end-organ damage and complications across the body. In psychiatry, discussions of the possible impact of delayed treatment on psychosis prognosis started in the early 1990s. Wyatt,1 reviewing the treatment of schizophrenia with antipsychotics, questioned whether there was something toxic about untreated psychosis that went beyond the immediate psychotic episode. This has been used to support the assertion that, similar to the rest of medicine, early intervention in the first onset of schizophrenia can improve long-term prognosis. And it has led to the development of first-episode services in many high-income countries. The aim is simple: to treat early and increase the likelihood of recovery. A further development based on the premise that treating people early could improve prognosis has been the trial of treatment of people in the prodrome of schizophrenia. A recent Cochrane review2 has concluded that there is some emerging evidence that this improves outcomes. These are success stories that may have already made a difference to the lives of patients, but, if we want to continue to improve services, we need to understand how our interventions work. Our lack of understanding of the mechanisms through which lack of treatment leads to poorer outcomes may make it difficult for us to develop prevention, screening, and timely, targeted early intervention as has proved effective in diabetes. If we could answer Wyatt’s question, and we knew what was toxic about untreated psychosis, we may be able to produce better treatment. Numerous, different studies have tried to shed light on the delay in untreated psychosis and prognosis. They have measured the association between the time untreated and subsequent symptoms, cognitive problems, and changes in the brain. Mechanisms have been suggested to explain these findings. As summarized by Rund,3 Wyatt1 believed that untreated psychosis was biologically toxic to the brain. Sheitman and Lieberman4 elaborated, claiming that the inability to regulate a presynaptic dopamine release in the limbic striatum and the prolonged sensitization and overstimulation resulted in people being refractory of treatment because of structural neuronal changes. Others have postulated that active psychosis may damage neuronal connectivity,5 while Wood et al6 believed that the impacts were through stress and the release of stress-related hormones. The focus for pathological deliberations, so far, has been very much on the brain. This flies in the face of increasing reports that the etiology of psychosis is multifactorial. There are fundamental biological processes that are important for brain function, but these are significantly influenced by psychological and social factors that mediate both brain development and subsequent brain function.7 There are associations between genetic endowment and risk of psychosis but also factors that are linked to the development of the brain, such as childhood trauma or early separation from a parent. Cannabis increases not only the risk of psychosis but also the risk is increased in those who are born or brought up in a city. Migration increases the risk of psychosis, but some migrant groups, specifically those who are exposed to discrimination because of their race, have the highest risk. And crucially, the literature reports that such risk factors do not operate independently. They interact. The impact of biological factors, such as cannabis or genes, are significantly influenced by psychological and social factors.7 A person’s risk of developing schizophrenia relies on an interplay between biological, psychological, and social risk factors at individual and ecological levels that interact over time.8 It may be that not only the causation of the disorder but also the progress and prognosis have a similarly wide-ranging etiology. If we can understand the social, psychological, and biological mechanisms through which untreated psychosis is associated with outcome, we may find new avenues for improving care. The best first-episode services instinctively offer biological, psychological, and social treatments. However, a more scientific approach would be to try to base service development on an understanding of the mechanisms through which disease progression occurs. If we knew the reasons why lack of treatment may lead to worse outcome, we may be able to develop a treatment approach based on science. This would help us to work out which treatments, deployed when, could offer the best prognosis, and whether there is a gap in our treatment repertoire. In this In Review, we want to consider the possible mechanisms by which psychosis could be neurotoxic. The aim is to start a discussion that will allow us to build a better understanding of the processes driving outcome. Dr Kelly K Anderson and colleagues9 from the Centre for Addiction and Mental Health have looked at postulated biological mechanisms, and Dr Ross M G Norman10 has tried to identify social mechanisms that may link untreated psychosis with outcomes. If we can link the findings from these papers together with similar studies from our partners in psychology, we may start to develop an understanding of the impacts of untreated psychosis that will help us to improve services.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,052
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,019
Score d'incertitude au seuil0,056

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,052
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,003
Communication savante0,0040,005
Science ouverte0,0010,001
Intégrité de la recherche0,0030,004
Charge utile insuffisante (le modèle a refusé de juger)0,0170,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,013
Tête enseignante GPT0,275
Écart entre enseignants0,262 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations8
Publié2014
Routes d'admission3
Résumé présentoui

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Même revueThe Canadian Journal of Psychiatry→Même sujetSchizophrenia research and treatment→Travaux en français237 207→