Notice bibliographique
Résumé
Computer-based brain-imaging technologies were introduced into medicine beginning in the 1970s, with the introduction of computed axial tomography (CAT or CT) and, subsequently, magnetic resonance imaging (MRI), magnetic resonance spectroscopy, functional MRI, positron emission tomography (PET), and single photon emission computed tomography (SPECT). These technologies have revolutionized the practice of neurology but, to date, have had less impact on the practice of psychiatry. Nevertheless, brain imaging has become an increasingly important area of psychiatric research over the past 30 years and is beginning to influence how we understand psychiatric illness and how it should be treated. Brain imaging arrived on psychiatry's doorstep when explanatory models for psychiatric illness were in transition. The introduction of the first generation of psychotropic medications challenged the field to address biological determinants of mental illness, as did the emergence of new research establishing that many major mental illnesses have a high degree of heritability. CT and MRI captured the imagination of the field by their ability to visually present the 3-dimensional structure of the brain-as if this might settle once and for all whether illnesses such as schizophrenia are really brain diseases. The search for the structural basis of schizophrenia offers a useful example of how brain imaging informs our understanding of the illnesses we psychiatrists treat. That CT scanning revealed chronically ill patients with schizophrenia to have enlarged lateral ventricles1 brought to the fore the need to understand the disorder's underlying neurobiology. Subsequent studies have led us to understand that structural brain changes are anything but simple; they point to a model of dis ease that should cause us to challenge many of our assumptions about schizophrenia. Although the magnitude of the ventricular enlargement and subsequently described gray matter volume deficits in schizophrenia2 had a medium effect size, further studies revealed that these differences were greatest in patients who had been ill for longer periods and in those who were more severely affected.3 The extent to which illness duration, compared with illness severity, contributes to the magnitude of structural brain abnormalities observed remains an important topic of debate, as these factors are often confounded. Several longitudinal studies have provided evidence for progressive brain changes in patients with schizophrenia,4,5 reinforcing the Kraepelinian conceptualization of schizophrenia as a progressive, deteriorating illness. However, other studies have failed to find that brain abnormalities significantly progressed over time,6,7 which should cause us to ask in what ways schizophrenia should be understood as a progressive illness. Longitudinal studies of patients presenting with a first episode of schizophrenia provide clear support for ongoing clinical improvement over time, rather than worsening of symptoms, cognition, and community functioning.8,9 Relapses, which are all too frequent in schizophrenia, are more determined by medication nonadherence than by the natural history of the illness. The magnitude of structural brain abnormalities and the degree to which they change over time may to some degree reflect risk but are likely to be only minor determinants of the variance in outcome. That some of the brain differences observed might be related to medication exposure, nutrition, endocrine changes, or differences in activity levels cannot yet be excluded.10 Thus, although some might assume that identifying structural brain abnormalities in patients with schizophrenia could contribute to our pessimism about treatment outcomes, the very modest magnitude of these findings, the overwhelming degree of overlap with the normal population, and the questionable progression of these changes over time should cause us to question this interpretation. How structural brain abnormalities relate to the biology of other major mental disorders such as depression, bipolar disorder, posttraumatic stress disorder, and anorexia nervosa has also been a major focus of psychiatric research. …
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,010 |
| Communication savante | 0,006 | 0,007 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,005 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».