3 The importance of sharing information on overdiagnosis for decision making? Experiences and perspectives from different countries
Notice bibliographique
Résumé
<h3></h3> Overdiagnosis, a known consequence of screening, is not an easy concept to grasp. Clinicians, who should be aware and understand its impact on healthcare decisions, often do not even recognize its existence. The same can be said for individuals that play key roles in the administration of health care or make policy. Raising concerns related to overdiagnosis can impact the relationship between well-intentioned clinicians and their patients or draw criticism to physicians who wants to foster change and limit the harms that health systems can inadvertently cause or contribute to. The Canadian Task Force on preventive health care (CTFPHC) develops clinical practice guidelines that supports primary care providers in delivering preventive health care. We will share the work the CTFPHC is doing to better understand what matters to patients when facing screening decisions. We will describe our systematic reviews reporting on patient’s values and preference and our iterative process that uses focus groups of patients to determine the importance of different outcomes, one of them being overdiagnosis. An example of how confusion about the meaning or the extent of overdiagnosis played a role in a misunderstanding between the Task force and a specific group of physicians will be discussed. We also wish to foster a discussion about how to define and explain overdiagnosis when there is no diagnosis (e.g. risk of fragility fracture). Experiences from other countries will also be discussed. <h3>Objectives</h3> To share experiences and strategies for sharing information about overdiagnosis and define an agenda for research on that topic. <h3>Method</h3> There will be four short presentation all related to the overarching theme. Then participants in the audience will be asked to split off into discussion groups to accomplish different tasks Try themselves to explain overdiagnosis (different scenarios will be provided: individual patient, groups, policy maker, learners, collegues, the head of a hospital). We will then ask them to reflect on the difficulties they encountered and about what strategies were helpful or not. Define what they see as the next steps from a research perspective to understand how to best communicate overdiagnosis? Reflect on possible strategies to influence policy? <h3>Results</h3> Participants will discuss and share ideas. The major themes emerging from that group knowledge will be shared through social media and possibly a blog or a letter to the editor. <h3>Conclusions</h3> Overdiagnosis is not an easy concept to grasp and even if awareness has increased, there are still many groups of people that have not heard of or simply don’t understand that concept. We will share and deepen some of the thinking happening to counter this state of fact.
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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 ».