Impact of nirogacestat on pain, a key symptom in patients with desmoid tumors (DT): Results from the phase 3 DeFi study.
Notice bibliographique
Résumé
11564 Background: Pain reduction is a key treatment goal in DT (aggressive fibromatosis): 60% of patients (pts) experience chronic pain. In the phase 3 DeFi trial, nirogacestat (NIRO; n = 70) significantly improved progression-free survival compared with placebo (PBO; n = 72) in pts with progressing DT (HR: 0.29 [95% CI, 0.15–0.55]; P< 0.001). Also as previously reported, NIRO significantly reduced pain severity by 1.50 points (on a 10-point scale) compared with PBO at cycle 10 (28-day cycles; P< 0.001) per the prespecified secondary endpoint of “worst pain” from the Brief Pain Inventory Short Form (BPI-SF). Additional aspects of pain were collected in DeFi to further characterize treatment impact and consistency across multiple pain assessment tools. Methods: In DeFi, pts completed 3 prespecified pain assessment tools through end of treatment: BPI-SF (worst pain), GOunder/Desmoid Tumor Research Foundation DEsmoid Symptom Scale (GODDESS-DTSS pain scale: worst pain, dull pain, shooting pain), European Organisation for Research and Treatment of Cancer Core Quality of Life Questionnaire (QLQ-C30 pain scale: pain, pain interference with daily activities). Change from baseline (BL) in pain scores was compared between arms; analyses included mixed models for repeated measures to compare change from BL and stratified Cochran-Mantel-Haenszel to compare proportions of pts with clinically meaningful pain reduction (defined using prespecified thresholds) at cycle 10. Cycle 10 was preselected to allow adequate time for a treatment effect to be observed. Results: Statistically significant and clinically meaningful pain reductions were observed with NIRO compared with PBO at cycle 10 across all assessment tools; statistically significant differences between arms occurred as early as cycle 2 and were sustained throughout treatment. At cycle 10, NIRO reduced mean BL pain per GODDESS-DTSS (0–10 range) by 1.78 points (SE = 0.26) and PBO increased pain by 0.32 points (SE = 0.27; P< 0.001). At cycle 10, NIRO reduced mean BL pain per QLQ-C30 (0–100 range) by 22.05 points (SE = 3.38) and PBO increased pain by 7.19 points (SE = 3.64; P< 0.001). Clinically meaningful pain reduction (by ≥2.0 points) per BPI-SF worst pain (0–10 range) was achieved by 72% of pts with NIRO vs 29% of pts with PBO at cycle 10 ( P< 0.001). Per GODDESS-DTSS, clinically meaningful pain reduction (by ≥1.9 points) was achieved by 62% of pts with NIRO vs 19% of pts with PBO at cycle 10 ( P= 0.002). Conclusions: Rapid, sustained, and consistent reductions in different aspects of pain were observed with NIRO compared with PBO across multiple assessment tools in pts with DT. Furthermore, a significantly greater proportion of pts achieved clinically meaningful reductions in pain with NIRO than with PBO. As pain is the most commonly reported symptom, pain reduction should be a key clinical trial endpoint and a key treatment goal in DT. Clinical trial information: NCT03785964 .
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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».