Are Antidepressants as Effective as Claimed? Yes, but …
Notice bibliographique
Résumé
(Can J Psychiatry 2007;52:98-99) Major depressive disorder (MDD) is a serious mental illness with a 1-year prevalence rate of 4.1% to 4.8% in Canada and a lifetime prevalence of 8%.' The human cost of the illness in terms of impaired social and emotional functioning cannot be emphasized enough, and the societal burden from an economic standpoint alone is substantial. According to the WHO, the disability levels caused by depression are even higher than those caused by other common medical conditions, including arthritis, hypertension, and diabetes.2 Emerging evidence also suggests an overlapping pathoetiology between mood disorders and other chronic medical conditions such as diabetes mellitus and cardiovascular disease. As such, few would argue with the need for treatments for depression. Although comparable outcomes have been reported for pharmacotherapy and evidence-based psychotherapies, antidepressant medications are the most available first-line treatments for moderate-to-severe major depressive episodes. Effectiveness and the Scope of the Debate For the purpose of this debate, current first-line selective serotonin reuptake inhibitors and dual-action agents will be considered to represent antidepressants and the term effective will reflect real-world outcome when these medications are prescribed under some form of measurement-based conditions. This is explicitly not a debate about efficacy and the difference between active treatment and placebo outcomes. It is implicitly about the evidence to support a disease-centred model of depression and the capacity to demonstrate that antidepressants alter the depressive substrate. According to Moncrieff and Cohen,3 the challenge in supporting a disease-centred model for MDD is to present evidence that drugs correct an abnormal brain state, that therapeutic results are related to a favourable impact on disease pathology, and that effects differ between patients and volunteers. These criteria are borrowed from other systemic disorders such as diabetes mellitus, where this model of disease and the therapeutic effects of treatments can be readily evaluated. Until recently, limited technology and understanding of brain complexity have delayed the application of these criteria to depression. Evidence to Support the Disease-Centred Model of Depression There is ample evidence at varying levels of analysis to make this case. At the neuropsychological level, altered cognition, executive function, and reward behaviour, as well as neurobiological findings including alterations to circadian rhythm, the hypothalamo-pituitary-adrenal axis, or the immune system, are frequently demonstrated in states of clinical depression. Studies of stress and depression have more recently focused on the effects of stress on neurotrophic factors implicated in mood disorders. Brain-derived neurotrophic factor (BDNF) is one protein, important in neuronal development and survival, that has been of particular interest insofar as both acute and chronic stress tend to decrease its expression. However, increasing data suggest that antidepressants upregulate the production of BDNF, enhancing hippocampal neurogenesis and promoting neuroplasticity.4 Still, the most germane evidence in support of the disease-centred model of depression and antidepressant effectiveness is derived from structural and functional neuroimaging studies. Structural imaging studies have demonstrated that recurrent episodes of depression and a longer duration of untreated illness correlate with decreased hippocampal volume and with neurocognitive impairment5; in contrast, the same relation did not appear in patients treated with antidepressants. Further, depression patients treated with antidepressants have shown improved performance on various neurocognitive measures, compared with untreated depression patients.6 At the level of functional neuroimaging, glucose metabolism studies are instructive in regard to all 3 requirements for the disease-centred model. …
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 | 0,010 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,006 |
| Communication savante | 0,005 | 0,010 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,008 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,007 |
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 ».