Systemic therapy clinical trial participation in patients with bladder and kidney cancers.
Notice bibliographique
Résumé
451 Background: Patient participation in clinical trials has led to numerous treatment advances in renal cell carcinoma (RCC) and urothelial carcinoma (UC) over the past decade. The rate of patient participation in RCC and UC trials and factors influencing participation are unknown. This study evaluates patient participation rates in RCC and UC clinical trials at a major United Kingdom cancer centre. Methods: All referrals to St Bartholomew’s Hospital (SBH) Genitourinary Cancer Department between Jan 2020 to Sept 2022 were reviewed. Patients with RCC or UC of any stage were included. Dates of consultation and follow up visits were cross-referenced with a list of systemic therapy clinical trials open at SBH from Jan 2020 to Oct 2024. The proportion of patients who a) had a trial available to them, b) entered trial screening, and c) were eligible for a trial, was determined. Multilevel mixed-effects logistic regression models were used to assess the likelihood of clinical trial screening and enrolment with adjustment for relevant baseline variables (age, cancer type, gender, line of therapy, and performance status [PS]). Results: 403 patients were included in the analysis: 215 RCC (44% stage I-III and 60% had or developed metastatic disease) and 188 UC (41% stage I-III and 69% had or developed metastatic disease). 63% (254/403) of patients had at least one eligibility opportunity to be screened for a trial during the follow up. 40% (161/403) consented to trial screening, and 30% (118/403) were enrolled into at least one trial. The table shows trial availability, screening, and enrolment by line of therapy (rates were similar between RCC and UC, data not shown). Variables associated with increased odds of entering trial screening were line of therapy (second line odds ratio (OR) 8.6 (2.3-31.8), p<0.01, third line OR 3.4 (1.3-9.0) p=0.02, compared to adjuvant) and UC vs RCC trials OR 2.4 (1.3-4.3) p<0.01. Poor PS decreased the odds of entering trial screening (OR 0.21 (0.1-0.5) p<0.01). Gender and age were not associated with screening rates. No variables were associated with trial enrolment after a patient had consented to screening. Conclusions: At a major UK clinical trial centre, 40% of patients with RCC or UC entered clinical trial screening and 30% participated. Most patient characteristics were not associated with increased screening except for PS. Screening rates were higher in later line treatment studies. The effect of ethnicity and randomisation will be presented at the meeting. These data highlight patient willingness to screen for trials when they are available. Clinical trial availability, screening rates, and enrollment rates by line of therapy for patients with RCC and UC. Neo/Adjuvant 1L 2L 3/4L Trial Available 45% (86/248) 58% (151/259) 34% (35/104) 66% (40/60) Screened 56% (48/86) 52% (78/151) 91% (32/35) 83% (33/40) Enrolled 73% (35/48) 68% (53/78) 63% (20/32) 82% (27/33)
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 | 0,014 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».