Antidepressant and Antipsychotic Drug Use and Cancer Risk: Protocol for an Overview of Systematic Reviews and Meta-Analyses
Notice bibliographique
Résumé
BACKGROUND: The relationship between cancer and central nervous system disorders has received increasing attention recently. Consequently, antipsychotics and antidepressants, commonly prescribed for conditions such as depression, bipolar disorder, and schizophrenia, have emerged as potential modulators of subsequent cancer risk. Previous studies have suggested that the use of these medications is associated with a decreased risk of cancer incidence and mortality, making them suitable candidates for drug repurposing. However, the potential therapeutic benefits do not extend to all cancer types, as some data suggest an increased risk for specific tumors. OBJECTIVE: This study aims to conduct a comprehensive review of systematic reviews and meta-analyses (review of reviews) that assess whether exposure to antidepressants or antipsychotics influences cancer incidence and mortality. METHODS: To provide a clear overview of this review, we have designed and registered the study protocol. Specifically, we will include systematic reviews and meta-analyses that examine the relationship between previous antipsychotic or antidepressant treatments and the subsequent cancer risk. The primary outcome will be the risk of cancer incidence and mortality (all malignant neoplasms) associated with exposure to psychopharmacological medications. Furthermore, secondary outcomes will include site-specific cancer incidence and mortality (eg, lung cancer). Literature searches will be conducted in multiple electronic databases (from their inception onwards), including PubMed/MEDLINE, Embase, and the Cochrane Database of Systematic Reviews. Three researchers will independently screen all citations, abstracts, and full-text articles. We will perform parallel search, selection, and extraction tasks using a large language model (GPT-4o; OpenAI). Data selection and extraction will involve both human reviewers and GPT-4o, whose performance will be validated through human evaluations. Thus, we will verify whether this type of tool can accelerate or even perform the tasks involved in a systematic review. The risk of bias and the quality of individual studies will be evaluated using appropriate tools. Subsequently, we will extract the summary association measures (eg, pooled relative risk, odds ratio, and hazard ratio) as reported in each included systematic review. Where available, we will summarize subgroup and sensitivity analyses as described by the authors. RESULTS: Planned searches will be conducted in various electronic databases from their creation until September 2025. No results are available or included in this protocol. The expected results will be published in 2026. CONCLUSIONS: This overview of systematic reviews and meta-analyses will provide an updated synthesis of the cancer risk associated with antipsychotic and antidepressant drugs. Furthermore, this study will examine factors that may explain potential study variations. Ultimately, these findings will be published in a peer-reviewed journal. TRIAL REGISTRATION: OSF Registries 10.17605/OSF.IO/5ACWH; https://osf.io/5acwh/overview. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/78596.
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 | 0,094 | 0,148 |
| Méta-épidémiologie (sens strict) | 0,009 | 0,007 |
| Méta-épidémiologie (sens large) | 0,023 | 0,039 |
| Bibliométrie | 0,018 | 0,021 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,008 | 0,008 |
| Science ouverte | 0,007 | 0,007 |
| Intégrité de la recherche | 0,008 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,066 | 0,009 |
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 ».