Treatments and ongoing monitoring for patients with neuroendocrine tumors, monitored by medical oncologists: Scan comparative data between advanced economies (AE) and emerging and developing economies (EDE).
Notice bibliographique
Résumé
e16200 Background: The Survey of Challenges in Access to Diagnostics and Treatment for NET Patients (SCAN) measured the delivery of healthcare to neuroendocrine tumor (NET) patients globally. This analysis focused on the treatments and follow-up received by NET patients who most often visited a medical oncologist (MO) for their ongoing monitoring, and compared results between Advanced Economies (AE) and Emerging and Developing Economies (EDE). Methods: During Sept-Nov 2019, 2359 NET patients and 436 healthcare professionals (HCPs) from 68 countries completed an online self-report survey, available in 14 languages, disseminated by NET patient group networks, NET medical societies and other INCA partners. Results: 1016 NET patients (43% of all NET patients globally) reported a MO as the HCP most often visited for their ongoing monitoring, 90% of which were from AE (N = 913) and 10% from EDE (N = 103). 108 MOs (25% of all HCPs) took part in the survey, 62% from EDE [67/108]. Primary NETs for this patient sub-group were most often GEP NETs, specifically small intestine, more often reported from AE (41%, 316/913) than EDE (20%, 21/103; p < 0.0001 by Chi-square), and pancreatic, more often reported from EDE (33%, 34/103) than AE (21%, 192/913). Other primary NETs included lung (AE [11%, 92/913], EDE [6%, 6/103]) and of unknown origin (AE [8%, 73/913], EDE [14%, 14/103]). The most common treatment received was somatostatin analogues (SSA) (AE [57%, 507/913], EDE [44%, 43/103]), followed by surgery (AE [16%, 140/913], EDE [17%, 16/103]) and oral chemotherapy (AE [14%, 120/913], EDE [18%, 17/103]). PRRT (AE [14%, 124/913], EDE [8%, 8/103]; p < 0.0001) was used more frequently in AE. Awareness of the 4 most frequently used treatments among MOs was 80% or greater. MOs reported similar availability of SSA (AE [95%, 39/41], EDE [96%, 64/67]) by economic areas, while lower availability of surgery (AE [98%, 40/41], EDE [88%, 59/67], oral chemotherapy (AE [98%, 40/41], EDE [82%, 55/67]) and PRRT (AE [81%, 33/41], EDE [60%, 40/67]; p < 0.0001) in EDE. NET patients reported CT scan as the most frequently used ongoing monitoring tool (AE [78%, 699/913], EDE [70%, 66/103]). Ga-68-labeled SSA PET/CT was used for slightly more than 1/3 of patients with no significant differences by regions (AE [38%, 337/913], EDE [30%, 28/103]). For these tools, awareness among MOs was 69% and above, while both awareness and availability were significantly lower in EDE. Multidisciplinary teams (MDT) were rarely used in AE NET patients (35%, 318/913), and in only 14% (14/103) of EDE. Conclusions: MOs play an essential role in NET patients’ follow-up, being the leading HCP for almost half of them. There is a critical need for a global standard of ongoing NET monitoring as data indicate significant differences in therapeutic and follow-up procedures between AE and EDE.
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 | 0,001 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».