Clinical Profile and Predictors of Survival in Carcinoma Penis Patients
Notice bibliographique
Résumé
BACKGROUND: Carcinoma penis is a rare neoplasm, and the literature is scarce on long-term survival and its predictors. The aim of the study was to determine the clinical profile and management patterns, identify predictors of survival, and the impact of education and rural/urban dwelling on survival. METHODS: Patients with a histological diagnosis of carcinoma penis from January 2015 to December 2019 were included in the study. Demographics, clinical profile, education status, primary residence address, and outcomes were obtained from the case records. Distance from the treatment centre was obtained from the postal code. The primary objectives were to assess relapse-free survival (RFS) and overall survival (OS). The secondary objectives were to identify the predictors of RFS and OS and to determine the clinical profile and treatment patterns in patients with carcinoma penis in India. Time-to-event was calculated by Kaplan-Meir analysis and survival was compared by the log-rank test. Univariate and multivariable Cox regression analyses were used to find independent predictors of relapse and mortality. Logistic regression analyses to examine the associations of rural residence, education status, and distance from the treatment centre with the relapse adjusting for measured confounding variables. RESULTS: Case records of 102 patients treated during the above period were retrieved. The median age was 55.5 (interquartile range [IQR] 42-65 years). Ulcero-proliferative growth (65%), pain (57%), and dysuria (36%) were the most common presenting features. Clinical examination or imaging revealed inguinal lymphadenopathy in 70.6% of patients, however, only 42% of these lesions were pathologically involved. A total of 58.8% of patients were from rural areas, 46.9% had no formal education, and 50.9% had a primary residence ≥100 km from the hospital. Patients with lower education and rural households had higher TNM stages and nodal involvement. Median RFS and OS were 57.6 months (15.8 months to not reached) and 83.9 months (32.5 months to not reached), respectively. On univariate analysis tumor stage, involvement of lymph nodes, T stage, performance status, and albumin was predictive for relapse and survival. However, on multivariate analysis, the stage remained the only predictor of RFS and nodal involvement, and metastatic disease was a predictor of OS. Education status, rural habitation, and distance from the treatment centre were not predictors for relapse or survival. CONCLUSIONS: Patients with carcinoma have locally advanced disease at presentation. Rural dwellings and lower education were associated with the advanced stage but did not have a significant bearing on the survival outcomes. The stage at diagnosis and nodal involvement is the most important predictor of RFS and OS.
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,002 | 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 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 ».