The utility of abbreviated patient‐reported outcomes for predicting survival in early stage colorectal cancer
Notice bibliographique
Résumé
BACKGROUND: Patient-reported outcomes (PROs) are increasingly used in clinical settings. Prior research suggests that PROs collected at baseline may be associated with cancer survival, but most of those studies were conducted in patients with breast or lung cancer. The objective of this study was to determine the correlation between prospectively collected PROs and cancer-specific outcomes in patients with early stage colorectal cancer. METHODS: Patients who had newly diagnosed stage II or III colorectal cancer from 2009 to 2010 and had a consultation at the British Columbia Cancer Agency completed the brief Psychosocial Screen for Cancer (PSSCAN) questionnaire, which collects data on patients' perceived social supports, quality of life (QOL), anxiety and depression, and general health. PROs from the PSSCAN were linked with the Gastrointestinal Cancers Outcomes Database, which contains information on patient and tumor characteristics, treatment details, and cancer outcomes. Cox regression models were constructed for overall survival (OS), and Fine and Gray regression models were developed for disease-specific survival (DSS). RESULTS: In total, 692 patients were included. The median patient age was 67 years (range, 26-95 years), and the majority had colon cancer (61%), were diagnosed with stage III disease (54%), and received chemotherapy (58%). In general, patients felt well supported and reported good overall health and QOL. On multivariate analysis, increased fatigue was associated with worse OS (hazard ratio [HR], 1.99; P = .00007) and DSS (HR, 1.63; P = .03), as was lack of emotional support (OS: HR, 4.36; P = .0003; DSS: HR, 1.92; P = .02). CONCLUSIONS: Although most patients described good overall health and QOL and indicated that they were generally well supported, patients who experienced more pronounced fatigue or lacked emotional support had a higher likelihood of worse OS and DSS. These findings suggest that abbreviated PROs can inform and assist clinicians to identify patients who have a worse prognosis and may need more vigilant follow-up. Cancer 2017;123:1839-1847. © 2017 American Cancer Society.
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 | 0,000 | 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 ».