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Enregistrement W4391727359 · doi:10.1093/neuonc/noae004

The greatest challenge for pediatric low-grade glioma

2024· editorial· en· W4391727359 sur OpenAlexaff
Daniel C. Moreira, Éric Bouffet, Ibrahim Qaddoumi

Notice bibliographique

RevueNeuro-Oncology · 2024
Typeeditorial
Langueen
DomaineMedicine
ThématiqueGlioma Diagnosis and Treatment
Établissements canadiensHospital for Sick Children
Organismes subventionnairesAmerican Lebanese Syrian Associated Charities
Mots-clésGliomaMedicinePsychologyCancer research

Résumé

récupéré en direct d'OpenAlex

Recently, the International Pediatric Low-Grade Glioma Coalition (iPLGGc) published a series of articles describing the landscape of pediatric low-grade glioma (pLGG) and 3 major challenges in the field.1 Nonetheless, when considering the context of care available for children with LGG across the world, additional challenges, possibly larger and more complex to resolve, must be brought to the attention of the pediatric neuro-oncology community. Of the 400 000 children in whom cancer develops each year, approximately 90% live in low- and middle-income countries (LMICs), where health systems are unprepared to manage the burden of pediatric cancer.2 The reality for these children is stark. Nearly 50% of children in whom cancer develops are never diagnosed, and of those who receive a diagnosis, global cure rates are estimated to be less than 40%.1,3 Survival of children with CNS tumors is probably even more dismal. Ultimately, the principal prognostic factor for children with cancer has nothing to do with biology but the country in which they live. Although lower survival rates for pLGG in LMICs are reported, some as low as 60%, robust data on outcomes are scarce.4 Furthermore, the reported incidences are extremely variable, with up to a 100-fold difference between high-income countries and LMICs, suggesting an enormous rate of underdiagnosis in LMICs.5 Due to limitations in population-based cancer registries (PBCRs), the global burden of pLGG is unknown. Fewer than 15% of pediatric patients worldwide are covered by quality PBCRs.5 Furthermore, in many PBCRs, benign tumors and tumors without morphologic confirmation are inconsistently captured.6 This clearly leads to under-reporting of pLGG, particularly optic pathway glioma. Without precise data, it is impossible to evaluate and modify the factors leading to divergent outcomes. The disparities in pLGG outcomes are rooted in inequalities in access to quality care, as many of the elements needed are not always available in LMICs. For example, pediatric neurosurgical care in LMICs is limited, with disparities in the infrastructure and essential services needed to provide neurosurgical care to children with CNS tumors.7 Furthermore, the gap between high- and low-resource settings’ abilities to provide comprehensive molecular evaluation is enlarging. In the field of pLGG, the shift toward molecular-based treatments is irrelevant to the care of most children. Without comprehensive diagnostics, patients who would benefit from targeted therapy cannot be identified. Furthermore, the use of targeted agents, ever more prevalent in the treatment of pLGG, is limited due to their availability and cost. However, such agents would be extremely valuable in resource-limited settings due to the reduced hospitalization and no impact on patient immunity associated with these treatments. Improving the outcomes of children with LGG through equitable access to the field’s scientific advances is possibly the greatest challenge for the pediatric neuro-oncology community. Data to help prioritize interventions to improve access to quality care for children with LGG are limited, thus uncertainty persists. Nonetheless, as a community, we have 2 essential challenges to address: increasing the number of children who receive a timely diagnosis and improving the outcomes of those who are treated. To expand access to quality care, a multidisciplinary, multisectoral dialog is needed at the national, regional, and global levels. Importantly, the World Health Organization (WHO) launched the Global Initiative for Childhood Cancer (GICC) in 2018, aiming to achieve at least 60% survival for pediatric patients with cancer worldwide. The GICC selected pLGG as 1 of 6 cancers to serve as a tracer to monitor the initiative’s impact. The GICC opens opportunities to highlight the needs of children with pLGG at the level of governments and ministries of health, allowing essential diagnostics and therapeutic elements to be included in national cancer control plans. Understanding the burden of pLGGs is essential to quantify the disparities and prioritize interventions. Investment in programs to increase the capacity of quality cancer registries is essential for policy-makers to make decisions on resource allocation and for clinicians to optimize care.8 Furthermore, awareness campaigns for pediatric cancer and the strengthening of referral networks would help with timely diagnosis and treatment of pLGG. In addition, among the many challenges for pediatric cancer care to overcome in LMICs is the availability and affordability of antineoplastic drugs.9 Although chemotherapeutic agents used to treat pLGG like vincristine, carboplatin, and vinblastine are on the WHO essential medicines list, commonly used targeted therapies have not been incorporated. The addition of agents relevant for the care of pLGG, like MEK inhibitors, should be sought as this would lead to increased access of these agents. Expanded use of targeted therapy and molecularly defined risk-stratified treatment must go hand in hand with access to molecular diagnostics. Implementing centralized national or regional molecular testing center would help to increase access to these resources.10 Children with pLGG who live in LMICs deserve better care and cannot be left behind as the field advances. Ultimately, our ability to cure pLGG should be limited only by our understanding of the biology of the disease, not by the availability of care. The authors declare no competing interests. This study was funded by the American Lebanese Syrian Associated Charities.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,028
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,025
Score d'incertitude au seuil0,037

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,028
Méta-épidémiologie (sens strict)0,0030,001
Méta-épidémiologie (sens large)0,0040,003
Bibliométrie0,0030,001
Études des sciences et des technologies0,0030,003
Communication savante0,0070,005
Science ouverte0,0030,002
Intégrité de la recherche0,0250,035
Charge utile insuffisante (le modèle a refusé de juger)0,0090,007

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.

Tête enseignante Opus0,022
Tête enseignante GPT0,320
Écart entre enseignants0,298 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations11
Publié2024
Routes d'admission1
Résumé présentnon

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