Quality evaluation of field safety notices of medical devices, across EU and non-EU countries
Notice bibliographique
Résumé
Abstract Introduction The EU Medical Device Regulation 2017/745 (MDR; article 87) requires device manufacturers to report to the relevant authorities any serious incident and any corrective action undertaken, through Field Safety Notices (FSN) whose content must be consistent in all Member States. Before MDR, due to the lack of harmonized global standards for reporting, FSNs were published independently by each country with different formats, styles, nomenclatures and languages. This heterogeneity makes it challenging to use such historical data for trend analysis in post-market surveillance (PMS). Purpose 1) To assess the quality of the FSNs issued by EU or non-EU national authorities for analysing historical trends. 2) To group countries based on such quality, and test for differences between EU and non-EU countries. Methods As part of the CORE-MD project coordinated by the ESC, all FSN information publicly available as HTML text (excluding linked PDFs) was retrieved automatically from national authorities’ websites, using the CORE-MD PMS tool [1]. For each FSN, a score was assigned to each field of interest (1 or 2, according to importance for building trends) (see Figure 1), and a Percentage Score (PS) computed as the % of the ratio between the scores’ sum and the maximum possible score (10). Based on PS, FSNs were categorized as Excellent (≥90), Very Good (80≤PS<90), Good (70≤PS<80), Medium (60≤PS<70), or Unqualified (<60). For each country, the distribution of FSN categories was computed. Hierarchical Agglomerative Clustering (HAC) was used to group countries based on their similarities, and differences between EU and non-EU countries were assessed by Mann-Whitney U test. Results 126,405 FSNs published before 31/12/2022 were retrieved and analyzed. The % of FSN in which each field was available is shown by country in Figure 1. Manufacturer and Device were clearly described, but more detailed device information was often absent. The Description, with the reason for publishing the FSN, and the Device Category, allowing nomenclature categorization, were provided only by a few countries. The % of FSNs in each quality category is reported in Figure 2: only Italy provided some FSNs (31%) classified as Excellent. The HAC analysis identified three clusters: "high-quality FSNs" (Czechia, Denmark, Italy, Sweden, the Netherlands, and the USA), "moderate-quality FSNs" (Australia, Canada, Greece, Ireland, Latvia, Slovenia, Spain, and the UK), and "low-quality FSNs" (Croatia, Estonia, France, Germany, Poland, and Portugal). There was no difference between EU and non-EU countries. Conclusions A significant discrepancy was observed in the quality of FSNs retrieved from different countries, highlighting the difficulty in using such data to analyse trends, for example in reports of cardiovascular devices. This study reinforces the need for a global minimum reporting standard, to facilitate more effective PMS.Figure 1Figure 2
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 | 0,073 | 0,182 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,015 | 0,014 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».