RENEB Inter-Laboratory comparison 2017: limits and pitfalls of ILCs
Notice bibliographique
Résumé
Purpose In case of a mass-casualty radiological event, there would be a need for networking to overcome surge limitations and to quickly obtain homogeneous results (reported aberration frequencies or estimated doses) among biodosimetry laboratories. These results must be consistent within such network. Inter-laboratory comparisons (ILCs) are widely accepted to achieve this homogeneity. At the European level, a great effort has been made to harmonize biological dosimetry laboratories, notably during the MULTIBIODOSE and RENEB projects. In order to continue the harmonization efforts, the RENEB consortium launched this intercomparison which is larger than the RENEB network, as it involves 38 laboratories from 21 countries. In this ILC all steps of the process were monitored, from blood shipment to dose estimation. This exercise also aimed to evaluate the statistical tools used to compare laboratory performance.Materials and methods Blood samples were irradiated at three different doses, 1.8, 0.4 and 0 Gy (samples A, C and B) with 4-MV X-rays at 0.5 Gy min−1, and sent to the participant laboratories. Each laboratory was requested to blindly analyze 500 cells per sample and to report the observed frequency of dicentric chromosomes per metaphase and the corresponding estimated dose.Results This ILC demonstrates that blood samples can be successfully distributed among laboratories worldwide to perform biological dosimetry in case of a mass casualty event. Having achieved a substantial harmonization in multiple areas among the RENEB laboratories issues were identified with the available statistical tools, which are not capable to advantageously exploit the richness of results of a large ILCs. Even though Z- and U-tests are accepted methods for biodosimetry ILCs, setting the number of analyzed metaphases to 500 and establishing a tests’ common threshold for all studied doses is inappropriate for evaluating laboratory performance. Another problem highlighted by this ILC is the issue of the dose-effect curve diversity. It clearly appears that, despite the initial advantage of including the scoring specificities of each laboratory, the lack of defined criteria for assessing the robustness of each laboratory’s curve is a disadvantage for the ‘one curve per laboratory’ model.Conclusions Based on our study, it seems relevant to develop tools better adapted to the collection and processing of results produced by the participant laboratories. We are confident that, after an initial harmonization phase reached by the RENEB laboratories, a new step toward a better optimization of the laboratory networks in biological dosimetry and associated ILC is on the way.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 | 0,000 |
| 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 ».