Is There a Role for Antidepressant and Antipsychotic Pharmacogenetics in Clinical Practice in 2014?
Notice bibliographique
Résumé
The recent release of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, has been associated with much hand-wringing and many comments about the lack of progress on the understanding of biological mechanisms underlying mental disorders and their treatment.1 Publications, both in the general press and in scientific journals, decried psychiatry for being falsely advertised as a science. Among many similar examples, a columnist in The New York Times stated,The problem is that the behavorial sciences like psychiatry are not really sciences; they are semi-sciences. [ . . . ] Mental diseases are not really understood the way, say, liver diseases are understood, as a pathology of the body and its tissues and cells.2He went on, quoting Martin Seligman, a past president of the American Psychological Association, in this statement:I have found that drugs and therapy offer disappointingly little additional help for the mentally ill than they did 25 years ago-despite billions of dollars in funding.This viewpoint, by the general public and by health professionals, is that the impact of scientific progress in psychiatry has not met up with its promise. Contemporary psychiatric treatments are as efficacious as (or more efficacious than) most other nonsurgical treatments offered by modern medicine. With a number-needed-to-treat typically of 3 to 6, the efficacy of antidepressants (ADs) and antipsychotics (APs) is superior or similar to the efficacy of any medications used to treat general medical conditions except for antibiotics.3 Nevertheless, it can be argued that, except for better tolerability, there has been only incremental progress in the development of newer ADs and APs since the original, serendipitous discoveries of iproniazid, imipramine, and chlorpromazine in the 1950s.There are many possible reasons for this negative perception regarding progress in psychiatry, including a bias about patients with mental illness. Because of their salience, people are painfully aware of those who are not treated or are not doing well, and they are not aware of those who are successfully treated and are doing well; we see and hear the untreated, acutely psychotic shoeless man screaming in the street or our loved ones who have not yet responded to their medications. By contrast, the mental illnesses of our colleagues or friends who have remitted with treatment are invisible. Another reason for these negative perceptions is that, when used under usual care conditions, psychotropics typically yield mediocre, or even poor, outcomes. For instance, several studies published during the past decade have shown that less than one-half of depressed patients who were treated under usual care conditions by a community psychiatrist or a family physician achieved good outcomes.4,5Some of these disappointing outcomes are due to the variable efficacy and the early side effects of ADs and APs. Given the tepid faith in the therapeutic powers of these medications, patients and health professionals are unable or unwilling to stay the course in the face of delays in response or unpleasant side effects, leading to premature discontinuation and, consequently, perceived lack of benefit. Innovative models of care incorporating more intensive education and support for patients, health professionals, and family caregivers (for example, collaborative care for major depressive disorder or assertive community treatment for schizophrenia) can counteract these factors and markedly increase the number of patients who take, tolerate, and respond to psychotropics.5 A complementary approach that could increase the number of patients who take and stay the course with currently available psychotropics is to preferentially use medications that are better tolerated. This can be done either by implementing medication algorithms that promote the systematic use of psychotropics that are statistically more likely to be better tolerated (that is, medications with lower overall rates of discontinuation associated with adverse effects) or by trying to match specific patients with specific medications. …
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 | 0,029 | 0,117 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,004 |
| Communication savante | 0,006 | 0,008 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,008 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,004 |
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 ».