Outcomes of Different Quality of Life Assessment Modalities After Breast Cancer Therapy
Notice bibliographique
Résumé
Importance: Improvement in clinical understanding of the priorities of patients with breast cancer (BC) regarding postoperative aesthetic outcomes (AOs) is needed. Objective: To assess expert panel and computerized evaluation modalities against patient-reported outcome measures (PROMs), the gold standard of AO assessment, in patients after surgical management of BC. Data Sources: Embase, MEDLINE, PsycINFO, PubMed, the Cochrane Central Register of Controlled Trials, the World Health Organization International Clinical Trials Registry Platform, and ClinicalTrials.gov were interrogated from inception through August 5, 2022. Search terms included breast conserving AND aesthetic outcome AND breast cancer. Ten observational studies were eligible for inclusion, with the earliest date of database collection on December 15, 2022. Study Selection: Studies with at least 1 pairwise comparison (PROM vs expert panel or PROM vs computerized evaluation with Breast Cancer Conservation Treatment cosmetic results [BCCT.core] software) were considered eligible if they included patients who received BC treatment with curative intent. Studies reporting solely on risk reduction or benign surgical procedures were excluded to ensure transitivity. Data Extraction and Synthesis: Two independent reviewers extracted study data with an independent cross-check from a third reviewer. The quality of included observational studies was assessed using the Newcastle-Ottawa Scale, and the level of evidence quality was assessed using the Grading of Recommendations Assessment, Development and Evaluation tool. Confidence in network meta-analysis results was analyzed with the Confidence in Network Meta-analysis semiautomated tool. Effect size was reported using random-effects odds ratios (ORs) and cumulative ratios of ORs with 95% credibility intervals (CrIs). Main Outcomes and Measures: The primary outcome of this network meta-analysis was modality (expert panel or computer software) discordance from PROMs. Four-point Likert responses across PROMs, expert panel assessment, and BCCT.core evaluation of AOs were assessed. Results: A total of 10 observational studies including 3083 patients (median [IQR] age, 59 [50-60] years; median [range] follow-up, 39.0 [22.5-80.5] months) with reported AOs were assessed and homogenized in 4 distinct Likert response groups (excellent, very good, satisfactory, and bad). Overall network incoherence was low (χ22 = 0.35; P = .83). Overall, panel and software modalities graded AO outcomes worse than PROMs. Specifically, for excellent vs all other responses, the panel to PROM ratio of ORs was 0.30 (95% CrI, 0.17-0.53; I2 = 86%) and the BCCT.core to PROM ratio of ORs was 0.28 (95% CrI, 0.13-0.59; I2 = 95%), while the BCCT.core to panel ratio of ORs was 0.93 (95% CrI, 0.46-1.88; I2 = 88%). Conclusions and Relevance: In this study, patients scored AOs higher than both expert panels and computer software. Standardization and supplementation of expert panel and software AO tools with racially, ethnically, and culturally inclusive PROMs is needed to improve clinical evaluation of the journey of patients with BC and to prioritize components of therapeutic outcomes.
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) | 0,001 | 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) | 0,001 | 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 ».