Cancer studies based on secondary data analysis of the Taiwan's National Health Insurance Research Database
Notice bibliographique
Résumé
There has been a surge in the academic publication output based on secondary analyses of the data from the Taiwan's National Health Insurance claim records. It has become a challenge to comprehend such a rapid expansion of the literature. Therefore, this study aimed to explore the conceptual content of National Health Insurance Research Database-based cancer research, using the abstract of articles extracted from PubMed between 2002 and 2015. Search terms including "National Health Insurance Research Database (NHIRD) AND Taiwan," "Taiwan AND population-based," and "Taiwan AND nationwide" were used to search in PubMed with the publication date limited to between 1997 and 2015. The retrieved articles were manually screened to retain only those that were cancer-related and were based on secondary data analysis of the NHIRD. A total 589 articles were selected for subsequent text mining using the R software. Among the 589 articles, the top 5 most studied cancer types were breast (16.3%), lung (11.4%), colorectal (10.4%), liver (8.3%), and prostate (7.5%). The articles that received the highest number of citations by PubMed Central articles were cited 92 times. The top 3 most frequently occurred keywords in the abstracts of the 589 articles were cancer, patient, and risk, with 3670, 2535, and 1652 times, respectively. Analysis of key conception indicated that the most common conceptions were diabetes, survival, breast cancer, lung cancer, and colorectal cancer. In conclusion, in this study of 589 published articles on secondary data analysis of the NHIRD, indexed by PubMed between 2002 and 2015, we found that while the risk factors of cancer, treatment of cancer, and survival of cancer patients were popular research topics, end-of-life cancer care issues were less studied. Further studies should explore these areas since they are as important as treatment of the disease itself for many patients.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».