Understanding Treatment Preferences In Patients with Primary Immune Thrombocytopenia Contemplating Splenectomy: A Qualitative Study
Notice bibliographique
Résumé
Abstract Abstract 392 Introduction Primary immune thrombocytopenia (ITP) is a common autoimmune bleeding disorder characterized by low platelet counts and an increased risk of bleeding. Recent consensus recommendations on ITP management (Provan, Blood 2010) emphasize the need for individualized treatment strategies based on patient preference; however little is known about which treatments patients prefer and why. Although splenectomy is most likely to induce a durable remission, uptake of splenectomy by patients and physicians is variable and a general tendency towards splenectomy avoidance has recently been observed. The objective of this study was to better understand patient preference and the factors affecting patients' decision for or against splenectomy. Methods We designed an exploratory qualitative interview study. Criterion sampling was used to identify eligible patients 18 years of age or older who were diagnosed with relapsed (lasting 3 – 12 months) or chronic (lasting longer than 12 months) primary ITP and who had been offered splenectomy as a treatment option by their physician, until data saturation was achieved. One to one, semi-structured interviews were conducted using an open-ended interview guide designed to investigate factors impacting splenectomy decision-making. Interview transcripts were coded independently in triplicate and interrater agreement was high. Major themes were identified from the data using a team analytic approach and audit trail. Results. Data saturation was achieved after 15 patients were interviewed; 6 were for splenectomy, 7 were against, and 2 were undecided. Patients were between the ages of 19 and 82 [median 43 years; interquartile range (IQR), 31 – 61] and 9 (60%) were female. Median duration of ITP was 49 months (IQR 13 – 113); patients had received a median of 2 prior treatments (IQR 2 – 3) and median platelet count at the time of the interview was 72 × 109/L (IQR 29 – 106). Four major themes were identified from the data about influences on treatment preferences: 1) patients' understanding of the ITP disease process; 2) patients' perception of the impact of ITP on their quality of life; 3) patients' understanding of the risks and benefits of treatments offered by their physician; and 4) patients' perception of splenectomy as a last resort. Patients were likely to accept splenectomy if their disease was perceived as having a negative impact on their quality of life. In general, patients had limited understanding of the cause of ITP and often misinterpreted the meaning of quoted probabilities of success with splenectomy. Conclusion Increased awareness of influences on patient treatment preferences will help physicians guide ITP patients through the complex decision-making process regarding splenectomy and can inform the design of decision-aids. Disclosures: Arnold: Hoffmann-LaRoche: Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees; Talecris: Honoraria. Kelton:Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees.
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 | 0,018 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,007 | 0,006 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 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 ».