“Piece by Piece” understanding of patient reported QOL and EV‐ICD: Response to letter from Vicentini and Rodorf
Notice bibliographique
Résumé
Vincentini and Rodorf highlight some additional points of interest related to our use of their subcutaneous ICD (S-ICD) patient sample as a comparison to the extra vascular implantalbe cardioverer defibillator (EV-ICD) patient group. As noted,1 we selected their study to use the historical norms on the Florida Patient Acceptance Survey2 comparisons as we believed that they were the most representative sample available in the literature. However, as with any historical comparison data set, important differences should be considered in interpretations, including differences in timepoint of patient reported outcomes (PROs) assessment (EV-ICD at 6 months vs. S-ICD at 12 months), composition of HFrEF versus non-HFrEF, age range, and BMI range. Each of these differences warrants consideration, but we suggest that the two most important of these differences related to PROs are the timing of assessment and average age of the sample. The primary purpose of PROs is to understand the patient experience broadly to refine care processes, increase patient acceptance, and improve health outcomes across time. At what point is the right time to evaluate a technology? The timing of PROs assessment does not garner much attention overall in the literature. Novel technologies produce a “wow” factor but are tempered if a poor patient experience results. Optimal assessment should sample the many domains of the patient experience with an orientation comparable to quality initiatives that are ongoing, recursive, and consistent in adapting processes to produce patient benefit. Clearly, the “acute phase of adjustment” (first 3 months) to an implantation or surgical intervention focuses more on pain and discomfort and possibly less on functional outcomes and psychological adjustment. However, we would suggest that “mid-range adjustment” (6–12 months) begins a rational period to sample the patient experience (Figure 1). Regular and ongoing assessment of the patient experience, at least annually, should also be strongly considered in research and in practice. The cardiac disease course changes and common psychosocial challenges present themselves, leading to changing evaluations by the patient. We agree that our use of the 6-month assessment likely produces slightly different samples of experience from the 12-month, but both have utility in evaluating patient experience and should be ongoing prospectively. Finally, the age of patients considering S-ICD and EV-ICD remains a point of interest and emphasis. The psychological adjustment of patients less than 50 years of age has been long been a point from our research.3 The psychosocial sequalae and disease presentation and course can be quite different than our older and more typical device patient cohorts4; both samples discussed here had relatively younger average ages (EV-ICD: 53 years vs. S-ICD: 56 years). These lower aged cohorts can likely be explained by the desire to prevent long term lead use in relatively young patients, but it also will magnify the potential importance of the patient experience and psychosocial impacts. Collectively, these samples may rate the patient experience “harder” because they have more challenges and higher expectations as they compare their experience to same age nondevice persons. Clearly, ongoing innovation and care planning that minimizes impact on lifestyle and optimizes the patient experience should be continuously rigorously pursued. We remain in agreement and acknowledge that PROs create value by providing a “big piece” of the unfolding story for technology assessment and innovation. Future research fills in the picture “piece by piece” with data.
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 | 0,008 | 0,043 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,025 | 0,027 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,003 |
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 ».