Equipment in the Global Radiology Environment: Why We Fail, How We Could Succeed
Notice bibliographique
Résumé
Purpose: This research aims to understand key problems and identify possible solutions in the market for radiology equipment in low- and middle-income countries. Methods and Materials: This paper uses simple descriptive statistics to summarize the results of responses from 574 radiologists from 52 countries surveyed in April-May 2017, and 15 hardware and software vendors from six countries surveyed in September-October 2017. Results: Radiologists surveyed came from both public and private sectors and were drawn from Radiological Society of North America (RSNA) members who, according to the survey results, appear to represent sites with more advanced technology. Virtually all the radiologists worked at sites where both X-ray and ultrasound were available, and the overwhelming majority (93%) had access to CT. Digital technology has gone worldwide: radiologists in all countries reported that digital radiography was either equally or more available than analog technologies. Sixty percent of radiologists said that they were “always” or “often” involved in the purchasing decisions in their institutions, but only 35% reported that they had the final say. According to the radiologists surveyed, the era of donated equipment is ending. Ninety-five percent felt that the disadvantages of donated equipment outweighed the cost savings. Training was a key concern both for radiologists and vendors. Radiologists felt that training was insufficient, materials left behind too complicated, online materials too limited, and follow-up from vendors insufficient. Vendors pointed out that the bidding process often excluded the cost of training and support and that many purchases are made through local distributors and they lack direct contact with vendors. Conclusion: While digital radiology is spreading throughout the surveyed countries, access to advanced imaging remains limited. Donated equipment is no longer a major solution to limited equipment availability. There is an opportunity for vendors and radiologists to work together to ensure that training, service and support are always included in purchases.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».