Notice bibliographique
Résumé
We sincerely thank Shamsi et al. for their comments on our article (1). Multidisciplinary cancer conferences provide a collaborative platform for cancer care, uniting specialists from diverse fields to enable a comprehensive evaluation of patients thereby promoting the development of more precise and tailored treatment strategies (2). In our meta-analysis of 134 287 patients, we found that multidisciplinary cancer conferences were associated with a statistically significantly increased overall survival across various cancer types. We agree with the authors that the integration of multidisciplinary cancer conferences is essential for the advancement of cancer treatment and advocate for the ongoing establishment of site-specific multidisciplinary tumor boards. Our study’s selection criteria only included comparative studies examining multidisciplinary cancer conference outcomes compared with nonmultidisciplinary cancer conference controls reporting overall survival data. Of the initial 3089 studies, many were excluded for not meeting these criteria, including noncomparative designs, lack of survival data, or a focus on administrative aspects of multidisciplinary cancer conferences. Covidence (Veritas Health Innovation, Melbourne, Australia), the tool we used for screening, is aligned with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, which do not necessitate documenting exclusion reasons at the title and abstract phase; thus, we unfortunately cannot specify the count of exclusions by reason at this stage (3). However, of the 140 studies that passed title and abstract screening, 43 (31%) were excluded because of noncomparative study design, 26 (19%) for reporting outcomes other than overall survival, and 12 (9%) for their focus on the administrative facets of multidisciplinary cancer conferences. We appreciate the authors’ comments on the variation in the timing and frequency of multidisciplinary cancer conferences and acknowledge that they are crucial factors that can influence patient outcomes. Our study conducted subgroup analyses on studies where the multidisciplinary team met more than once to follow treatment beyond the initial discussion of treatment planning, and our results suggest that the positive effect on overall survival was maintained in this subgroup; further research is required to determine the optimal utilization of multidisciplinary cancer conferences in this setting. We also acknowledge the limitations inherent in the inclusion of retrospective studies in our analyses. Prospective studies are warranted to explore the dynamics of patient selection and referral to multidisciplinary cancer conferences, aiming to minimize potential biases and enhance the representativeness of patient populations. Additionally, research into the operational aspects of multidisciplinary cancer conferences, including meeting frequency and interdisciplinary communication, will be critical in maximizing the efficacy of these conferences. We remain committed to the continuous advancement of multidisciplinary care models and hope our work will serve as a catalyst for ongoing investigations ultimately leading to the establishment of global best practices for multidisciplinary cancer conferences that are adaptable across diverse health-care systems and patient populations. The data underlying this article are available in the article and in its online supplementary material. Ryan S. Huang, MSc, MD(C) (Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Writing—original draft; Writing—review & editing) and Srinivas Raman, MD, MASc, FRCPC (Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Writing—original draft; Writing—review & editing). None. RSH: None. SR: Institutional grant funding—Astra Zeneca, Knight therapeutics. Honoraria—Bayer, Astra Zeneca, Tersera, Sanofi, Verity pharma.
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 | 0,002 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,036 | 0,030 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,011 |
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 ».