Systematic screening of perception of curability among patients with advanced cancer: A longitudinal analysis.
Notice bibliographique
Résumé
12110 Background: Clinicians do not routinely assess patients’ illness understanding despite its importance in decision making. Systematic screening of illness understanding is a novel approach that normalizes discussion of this sensitive topic, helps identify patients with information needs and allows clinicians to monitor and support their patients’ understanding over time. In this study, we examined the changes in perception of curability over time in patients who completed systematic screening at our supportive care clinic (SCC). Methods: We implemented universal electronic systematic screening of illness understanding in our SCC using two questions from the Prognosis and Treatment Perception Questionnaire at consultation and every 2 months. We included all advanced cancer patients who completed screening at their consultation and at least one follow-up visit within one year. The primary outcome was patients’ perception of curability, which was categorized as accurate if they reported the likelihood of cure as < 25%. Patients were grouped into one of four categories based on responses at their first and last SCC visits: accurate-accurate, accurate-inaccurate, inaccurate-accurate and inaccurate-inaccurate. We examined patient characteristics associated with the inaccurate-inaccurate group versus all others using univariate and multivariate logistic regression analysis. Results: 432 patients (mean age 58 [SD 13], female n=248 [57.4%], white n=331 [76.6%]) were included. The mean number of SCC visits was 2.69 [SD 0.9] and the median duration between the first and last SCC visits was 157 days [IQR 129-194]. At visits 1, 2, 3, 4 and 5+, 34% [147/432], 37% [159/432], 36% [71/197], 38% [30/78] and 46% [11/24] of patients had an accurate understanding of their curability (p=0.24), respectively. Comparing the first and last visit, 233 [54%] were inaccurate-inaccurate, 119 [28%] were accurate-accurate, 52 [12%] were inaccurate-accurate and 28 [6%] were accurate-inaccurate. Asian race and greater well-being at baseline were associated with being inaccurate-inaccurate (Table). Conclusions: Systematic screening identified that only ~1 in 3 advanced cancer patients had an accurate understanding of their curability at SCC consultation and this did not improve significantly over time. Certain subgroups were more likely to remain inaccurate at last follow-up. Our findings highlight the need to systematically screen for illness understanding and to work towards bridging information gaps with better communication and coping support. Multivariate analysis of patient characteristics associated with being in the inaccurate-inaccurate group. Patient Characteristic Odds Ratio 95% CI p-value Race (versus White) Asian 3.92 1.52-12.2 0.009 Black or African American 1.91 0.90-4.26 0.1 Edmonton Symptom Assessment System Well-Being 0.82 0.73-0.93 0.003
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 | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».