Audit, Feedback, and Education to Improve Quality and Outcomes in Transurethral Resection and Single-Instillation Intravesical Chemotherapy for Nonmuscle Invasive Bladder Cancer Treatment: Protocol for a Multicenter International Observational Study With an Embedded Cluster Randomized Trial
Notice bibliographique
Résumé
BACKGROUND: Nonmuscle invasive bladder cancer (NMIBC) accounts for 75% of bladder cancers. It is common and costly. Cost and detriment to patient outcomes and quality of life are driven by high recurrence rates and the need for regular invasive surveillance and repeat treatments. There is evidence that the quality of the initial surgical procedure (transurethral resection of bladder tumor [TURBT]) and administration of postoperative bladder chemotherapy significantly reduce cancer recurrence rates and improve outcomes (cancer progression and mortality). There is surgeon-reported evidence that TURBT practice varies significantly across surgeons and sites. There is limited evidence from clinical trials of intravesical chemotherapy that NMIBC recurrence rate varies significantly between sites and that this cannot be accounted for by differences in patient, tumor, or adjuvant treatment factors, suggesting that how the surgery is performed may be a reason for the variation. OBJECTIVE: This study primarily aims to determine if feedback on and education about surgical quality indicators can improve performance and secondarily if this can reduce cancer recurrence rates. Planned secondary analyses aim to determine what surgeon, operative, perioperative, institutional, and patient factors are associated with better achievement of TURBT quality indicators and NMIBC recurrence rates. METHODS: This is an observational, international, multicenter study with an embedded cluster randomized trial of audit, feedback, and education. Sites will be included if they perform TURBT for NMIBC. The study has four phases: (1) site registration and usual practice survey; (2) retrospective audit; (3) randomization to audit, feedback, and education intervention or to no intervention; and (4) prospective audit. Local and national ethical and institutional approvals or exemptions will be obtained at each participating site. RESULTS: The study has 4 coprimary outcomes, which are 4 evidence-based TURBT quality indicators: a surgical performance factor (detrusor muscle resection); an adjuvant treatment factor (intravesical chemotherapy administration); and 2 documentation factors (resection completeness and tumor features). A key secondary outcome is the early cancer recurrence rate. The intervention is a web-based surgical performance feedback dashboard with educational and practical resources for TURBT quality improvement. It will include anonymous site and surgeon-level peer comparison, a performance summary, and targets. The coprimary outcomes will be analyzed at the site level while recurrence rate will be analyzed at the patient level. The study was funded in October 2020 and began data collection in April 2021. As of January 2023, there were 220 hospitals participating and over 15,000 patient records. Projected data collection end date is June 30, 2023. CONCLUSIONS: This study aims to use a distributed collaborative model to deliver a site-level web-based performance feedback intervention to improve the quality of endoscopic bladder cancer surgery. The study is funded and projects to complete data collection in June 2023. TRIAL REGISTRATION: ClinicalTrials.org NCT05154084; https://clinicaltrials.gov/ct2/show/NCT05154084. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42254.
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 | 0,030 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,002 |
| Méta-épidémiologie (sens large) | 0,009 | 0,008 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,005 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,029 | 0,004 |
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 ».